diff --git a/.gitignore b/.gitignore
index 1c0094a..7bfc780 100644
--- a/.gitignore
+++ b/.gitignore
@@ -52,6 +52,8 @@ analysis_out/op/*
 # Which silent misses another model witnessed (#46). Small, and it carries the
 # unwitnessed list that is the gallery's actual work queue.
 !analysis_out/silent_witness.json
+!analysis_out/farfield_forensics.json
+!analysis_out/silent_activation.json
 
 # Generated tagging pages live next to their (committed) galleries; regenerate with
 # scripts/analysis/make_tagger.py rather than tracking a build artifact.
diff --git a/analysis_out/farfield_forensics.json b/analysis_out/farfield_forensics.json
new file mode 100644
index 0000000..c9cea5c
--- /dev/null
+++ b/analysis_out/farfield_forensics.json
@@ -0,0 +1,186 @@
+{
+  "threshold": 0.3,
+  "boundary_m": 18.0,
+  "judgeable_source_px": 30.0,
+  "match_tolerance": 0.2,
+  "counts": {
+    "far_gt": 700,
+    "far_hits": 453,
+    "far_silent": 83,
+    "unwitnessed_far": 46,
+    "rated": 37,
+    "below_floor": 9
+  },
+  "verdicts": {
+    "visible": 34,
+    "context-only": 2,
+    "unclear": 1
+  },
+  "per_split": {
+    "richmond": {
+      "far_silent": 13,
+      "unwitnessed": 5,
+      "rated": 4,
+      "stored_width_min": 4096,
+      "stored_width_max": 12288
+    },
+    "bend": {
+      "far_silent": 14,
+      "unwitnessed": 9,
+      "rated": 9,
+      "stored_width_min": 13312,
+      "stored_width_max": 16384
+    },
+    "clovis": {
+      "far_silent": 9,
+      "unwitnessed": 4,
+      "rated": 3,
+      "stored_width_min": 5760,
+      "stored_width_max": 5760
+    },
+    "morgantown": {
+      "far_silent": 6,
+      "unwitnessed": 2,
+      "rated": 1,
+      "stored_width_min": 4096,
+      "stored_width_max": 4096
+    },
+    "annapolis": {
+      "far_silent": 14,
+      "unwitnessed": 8,
+      "rated": 2,
+      "stored_width_min": 8000,
+      "stored_width_max": 8000
+    },
+    "paterson": {
+      "far_silent": 7,
+      "unwitnessed": 4,
+      "rated": 4,
+      "stored_width_min": 16384,
+      "stored_width_max": 16384
+    },
+    "gainesville": {
+      "far_silent": 20,
+      "unwitnessed": 14,
+      "rated": 14,
+      "stored_width_min": 16384,
+      "stored_width_max": 16384
+    }
+  },
+  "populations": {
+    "rated (reached the deck)": {
+      "n": 37,
+      "dist_q1_med_q3": [
+        23.593050428658366,
+        27.600046715707496,
+        39.39802964423609
+      ],
+      "px_q1_med_q3": [
+        19.855774091985104,
+        28.34337145668962,
+        33.157152723882376
+      ]
+    },
+    "below the floor (excluded)": {
+      "n": 9,
+      "dist_q1_med_q3": [
+        55.415367641067064,
+        116.69577442978571,
+        150.0
+      ],
+      "px_q1_med_q3": [
+        5.215189175235227,
+        6.703570717172544,
+        14.116632435829143
+      ]
+    },
+    "witnessed (never queued)": {
+      "n": 37,
+      "dist_q1_med_q3": [
+        23.35001330569262,
+        29.546807172079987,
+        36.30290837799241
+      ],
+      "px_q1_med_q3": [
+        21.548642002455033,
+        26.47590217546387,
+        33.50226683145265
+      ]
+    },
+    "ALL far-field silent misses": {
+      "n": 83,
+      "dist_q1_med_q3": [
+        23.641568560526633,
+        31.24504274559346,
+        42.60726425142641
+      ],
+      "px_q1_med_q3": [
+        18.360211340231608,
+        25.03687969495929,
+        33.08910634599019
+      ]
+    },
+    "far-field hits (for contrast)": {
+      "n": 453,
+      "dist_q1_med_q3": [
+        20.269464509192268,
+        21.346794177516905,
+        33.89167310588107
+      ],
+      "px_q1_med_q3": [
+        23.081727887595456,
+        36.646175991578325,
+        38.59393403957555
+      ]
+    }
+  },
+  "auc_rated_vs_unrated_px": 0.600470035252644,
+  "rated_median_px_percentile": 0.6204819277108434,
+  "auc_hit_vs_silent_px": 0.7181706960291497,
+  "bands": {
+    "18-25 m": {
+      "n_gt": 395,
+      "recall": 0.7772151898734178,
+      "silent": 25,
+      "rated": 14,
+      "visible": 12
+    },
+    "25-40 m": {
+      "n_gt": 226,
+      "recall": 0.5486725663716814,
+      "silent": 32,
+      "rated": 14,
+      "visible": 13
+    },
+    "40-150 m": {
+      "n_gt": 72,
+      "recall": 0.2916666666666667,
+      "silent": 22,
+      "rated": 9,
+      "visible": 9
+    },
+    "clamp>=150": {
+      "n_gt": 5,
+      "recall": 0.2,
+      "silent": 3,
+      "rated": 0,
+      "visible": 0
+    }
+  },
+  "matched_size_detection_rate_q1_med_q3": [
+    0.3076923076923077,
+    0.5700934579439252,
+    0.7402912621359223
+  ],
+  "above_horizon": {
+    "rated (reached the deck)": 0,
+    "below the floor (excluded)": 3,
+    "witnessed (never queued)": 0,
+    "ALL far-field silent misses": 3,
+    "far-field hits (for contrast)": 1
+  },
+  "rated_by_tier": {
+    "mapillary": 10,
+    "gsv": 27
+  }
+}
\ No newline at end of file
diff --git a/analysis_out/silent_activation.json b/analysis_out/silent_activation.json
new file mode 100644
index 0000000..ffdb45c
--- /dev/null
+++ b/analysis_out/silent_activation.json
@@ -0,0 +1,2571 @@
+{
+  "threshold": 0.3,
+  "null_trials": 200,
+  "null_seed": 20260731,
+  "n": 128,
+  "skipped_no_imagery": 0,
+  "model": "projectsidewalk/rampnet-model",
+  "tta": false,
+  "results": [
+    {
+      "city": "annapolis",
+      "pano": "1040516234061456",
+      "x": 0.06834458144797188,
+      "y": 0.5705305826510851,
+      "field": "near",
+      "dist_m": 11.1,
+      "px": 70.5,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.43735,
+      "null_pct": 0.75,
+      "null_med": 0.09615,
+      "null_p95": 0.66166,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.0,
+      "act_at_site": 0.16377,
+      "nearest_peak_px": 30.0,
+      "nearest_peak_score": 0.518
+    },
+    {
+      "city": "annapolis",
+      "pano": "1040516234061456",
+      "x": 0.07834260319860353,
+      "y": 0.5472727272727272,
+      "field": "near",
+      "dist_m": 16.7,
+      "px": 46.8,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.51325,
+      "null_pct": 0.805,
+      "null_med": 0.07194,
+      "null_p95": 0.62719,
+      "above_own_null_p95": false,
+      "argmax_off_px": 21.7,
+      "act_at_site": 0.18835,
+      "nearest_peak_px": 22.5,
+      "nearest_peak_score": 0.518
+    },
+    {
+      "city": "annapolis",
+      "pano": "1074557820562424",
+      "x": 0.3886869118430398,
+      "y": 0.6736363821318655,
+      "field": "near",
+      "dist_m": 4.1,
+      "px": 189.9,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.02373,
+      "null_pct": 1.0,
+      "null_med": 0.00237,
+      "null_p95": 0.02284,
+      "above_own_null_p95": true,
+      "argmax_off_px": 22.4,
+      "act_at_site": 0.01009,
+      "nearest_peak_px": 41.6,
+      "nearest_peak_score": 0.097
+    },
+    {
+      "city": "annapolis",
+      "pano": "1074557820562424",
+      "x": 0.6681082605432581,
+      "y": 0.5894862321196448,
+      "field": "near",
+      "dist_m": 8.7,
+      "px": 90.4,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.03074,
+      "null_pct": 0.81,
+      "null_med": 0.01836,
+      "null_p95": 0.07319,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.4,
+      "act_at_site": 0.00146,
+      "nearest_peak_px": 37.6,
+      "nearest_peak_score": 0.065
+    },
+    {
+      "city": "annapolis",
+      "pano": "1074557820562424",
+      "x": 0.6844444876006155,
+      "y": 0.566969696969697,
+      "field": "near",
+      "dist_m": 11.7,
+      "px": 66.8,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.00593,
+      "null_pct": 0.39,
+      "null_med": 0.01071,
+      "null_p95": 0.07319,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.5,
+      "act_at_site": 0.00262,
+      "nearest_peak_px": 50.3,
+      "nearest_peak_score": 0.065
+    },
+    {
+      "city": "annapolis",
+      "pano": "1074557820562424",
+      "x": 0.7112355588852785,
+      "y": 0.5416619902731012,
+      "field": "far",
+      "dist_m": 19.0,
+      "px": 41.2,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.01281,
+      "null_pct": 0.505,
+      "null_med": 0.01246,
+      "null_p95": 0.06504,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.4,
+      "act_at_site": 0.00209,
+      "nearest_peak_px": 77.6,
+      "nearest_peak_score": 0.065
+    },
+    {
+      "city": "annapolis",
+      "pano": "1265191387234427",
+      "x": 0.45847228033833237,
+      "y": 0.5232714292347035,
+      "field": "far",
+      "dist_m": 34.1,
+      "px": 22.9,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.11185,
+      "null_pct": 0.875,
+      "null_med": 0.00197,
+      "null_p95": 0.72167,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.3,
+      "act_at_site": 0.04899,
+      "nearest_peak_px": 31.5,
+      "nearest_peak_score": 0.166
+    },
+    {
+      "city": "annapolis",
+      "pano": "263456786691932",
+      "x": 0.5268878662384862,
+      "y": 0.49995505498717574,
+      "field": "far",
+      "dist_m": 150.0,
+      "px": 5.2,
+      "group": "below_floor",
+      "verdict": null,
+      "act": 0.13246,
+      "null_pct": 0.9,
+      "null_med": 0.00153,
+      "null_p95": 0.49731,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.4,
+      "act_at_site": 0.00845,
+      "nearest_peak_px": 42.2,
+      "nearest_peak_score": 0.826
+    },
+    {
+      "city": "annapolis",
+      "pano": "263456786691932",
+      "x": 0.4183953537314284,
+      "y": 0.4974932147254313,
+      "field": "far",
+      "dist_m": 150.0,
+      "px": 5.2,
+      "group": "below_floor",
+      "verdict": null,
+      "act": 0.38089,
+      "null_pct": 0.97,
+      "null_med": 0.00131,
+      "null_p95": 0.19137,
+      "above_own_null_p95": true,
+      "argmax_off_px": 22.4,
+      "act_at_site": 0.02107,
+      "nearest_peak_px": 33.2,
+      "nearest_peak_score": 0.719
+    },
+    {
+      "city": "annapolis",
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+      "x": 0.49330807772549706,
+      "y": 0.5068181818181818,
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+      "dist_m": 116.7,
+      "px": 6.7,
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+      "verdict": null,
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+      "null_pct": 0.74,
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+      "null_p95": 0.61309,
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+      "argmax_off_px": 22.2,
+      "act_at_site": 0.01469,
+      "nearest_peak_px": 61.8,
+      "nearest_peak_score": 0.575
+    },
+    {
+      "city": "annapolis",
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+      "field": "far",
+      "dist_m": 107.6,
+      "px": 7.3,
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+      "act": 0.48864,
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+      "null_p95": 0.59463,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.4,
+      "act_at_site": 0.13056,
+      "nearest_peak_px": 31.1,
+      "nearest_peak_score": 0.55
+    },
+    {
+      "city": "annapolis",
+      "pano": "846876249550191",
+      "x": 0.6507399277472887,
+      "y": 0.5029791914869305,
+      "field": "far",
+      "dist_m": 150.0,
+      "px": 5.2,
+      "group": "below_floor",
+      "verdict": null,
+      "act": 0.00131,
+      "null_pct": 0.495,
+      "null_med": 0.00159,
+      "null_p95": 0.72933,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.5,
+      "act_at_site": 0.0,
+      "nearest_peak_px": 118.7,
+      "nearest_peak_score": 0.086
+    },
+    {
+      "city": "annapolis",
+      "pano": "865533918497811",
+      "x": 0.5506606758523545,
+      "y": 0.509002398377811,
+      "field": "far",
+      "dist_m": 88.4,
+      "px": 8.9,
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+      "act": 0.0567,
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+      "null_med": 0.00068,
+      "null_p95": 0.10336,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.1,
+      "act_at_site": 0.02945,
+      "nearest_peak_px": 32.0,
+      "nearest_peak_score": 0.073
+    },
+    {
+      "city": "annapolis",
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+      "px": 46.4,
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+      "null_p95": 0.06203,
+      "above_own_null_p95": true,
+      "argmax_off_px": 22.5,
+      "act_at_site": 0.11586,
+      "nearest_peak_px": 31.4,
+      "nearest_peak_score": 0.64
+    },
+    {
+      "city": "annapolis",
+      "pano": "877430540067191",
+      "x": 0.4602280449077488,
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+      "field": "far",
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+      "px": 37.8,
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+      "act": 0.61482,
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+      "null_med": 0.00059,
+      "null_p95": 0.68466,
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+      "argmax_off_px": 22.4,
+      "act_at_site": 0.19303,
+      "nearest_peak_px": 25.1,
+      "nearest_peak_score": 0.64
+    },
+    {
+      "city": "annapolis",
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+      "x": 0.543034271131033,
+      "y": 0.5143504507599699,
+      "field": "far",
+      "dist_m": 55.4,
+      "px": 14.1,
+      "group": "below_floor",
+      "verdict": null,
+      "act": 0.35867,
+      "null_pct": 0.91,
+      "null_med": 0.00093,
+      "null_p95": 0.64023,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.1,
+      "act_at_site": 0.13544,
+      "nearest_peak_px": 33.4,
+      "nearest_peak_score": 0.685
+    },
+    {
+      "city": "annapolis",
+      "pano": "900958664931957",
+      "x": 0.45630241240838754,
+      "y": 0.5082816065558751,
+      "field": "far",
+      "dist_m": 96.1,
+      "px": 8.1,
+      "group": "below_floor",
+      "verdict": null,
+      "act": 0.19369,
+      "null_pct": 0.765,
+      "null_med": 0.00145,
+      "null_p95": 0.64171,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.0,
+      "act_at_site": 0.03081,
+      "nearest_peak_px": 40.1,
+      "nearest_peak_score": 0.907
+    },
+    {
+      "city": "annapolis",
+      "pano": "928118321383655",
+      "x": 0.9267534093454298,
+      "y": 0.5339254534343074,
+      "field": "far",
+      "dist_m": 23.4,
+      "px": 33.5,
+      "group": "rated",
+      "verdict": "visible",
+      "act": 0.07621,
+      "null_pct": 0.815,
+      "null_med": 0.0,
+      "null_p95": 0.38994,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.2,
+      "act_at_site": 0.01318,
+      "nearest_peak_px": 42.1,
+      "nearest_peak_score": 0.636
+    },
+    {
+      "city": "annapolis",
+      "pano": "967427853833872",
+      "x": 0.784888461045941,
+      "y": 0.5372327375413968,
+      "field": "far",
+      "dist_m": 21.3,
+      "px": 36.8,
+      "group": "rated",
+      "verdict": "visible",
+      "act": 0.10143,
+      "null_pct": 0.69,
+      "null_med": 0.00459,
+      "null_p95": 0.69251,
+      "above_own_null_p95": false,
+      "argmax_off_px": 21.9,
+      "act_at_site": 0.00894,
+      "nearest_peak_px": 32.9,
+      "nearest_peak_score": 0.184
+    },
+    {
+      "city": "annapolis",
+      "pano": "967427853833872",
+      "x": 0.8266666851621686,
+      "y": 0.5766666666666667,
+      "field": "near",
+      "dist_m": 10.2,
+      "px": 76.9,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.59154,
+      "null_pct": 0.865,
+      "null_med": 0.01575,
+      "null_p95": 0.68965,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.5,
+      "act_at_site": 0.07891,
+      "nearest_peak_px": 25.0,
+      "nearest_peak_score": 0.65
+    },
+    {
+      "city": "annapolis",
+      "pano": "997534874804455",
+      "x": 0.573055605680311,
+      "y": 0.5427722231683144,
+      "field": "far",
+      "dist_m": 18.5,
+      "px": 42.3,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.7841,
+      "null_pct": 1.0,
+      "null_med": 0.00065,
+      "null_p95": 0.01352,
+      "above_own_null_p95": true,
+      "argmax_off_px": 22.1,
+      "act_at_site": 0.24718,
+      "nearest_peak_px": 24.9,
+      "nearest_peak_score": 0.816
+    },
+    {
+      "city": "bend",
+      "pano": "0SgaioiGd5Yi_5wc6m-nJg",
+      "x": 0.5369325345142446,
+      "y": 0.5218091707287849,
+      "field": "far",
+      "dist_m": 36.4,
+      "px": 21.5,
+      "group": "rated",
+      "verdict": "visible",
+      "act": 0.65561,
+      "null_pct": 1.0,
+      "null_med": 0.00068,
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+      "x": 0.9712878880356297,
+      "y": 0.5335353504527698,
+      "field": "far",
+      "dist_m": 23.6,
+      "px": 33.1,
+      "group": "rated",
+      "verdict": "visible",
+      "act": 0.52351,
+      "null_pct": 0.72,
+      "null_med": 0.01795,
+      "null_p95": 0.95893,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.4,
+      "act_at_site": 0.07301,
+      "nearest_peak_px": 33.1,
+      "nearest_peak_score": 0.894
+    },
+    {
+      "city": "richmond",
+      "pano": "1288163659019705",
+      "x": 0.2791919130267519,
+      "y": 0.6537373675722065,
+      "field": "near",
+      "dist_m": 4.8,
+      "px": 164.1,
+      "group": "rated",
+      "verdict": "visible",
+      "act": 0.12626,
+      "null_pct": 0.86,
+      "null_med": 0.00255,
+      "null_p95": 0.36649,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.4,
+      "act_at_site": 0.01046,
+      "nearest_peak_px": 42.9,
+      "nearest_peak_score": 0.512
+    },
+    {
+      "city": "richmond",
+      "pano": "1338631107629110",
+      "x": 0.5325252463600852,
+      "y": 0.4973737312085701,
+      "field": "far",
+      "dist_m": 150.0,
+      "px": 5.2,
+      "group": "below_floor",
+      "verdict": null,
+      "act": 0.7454,
+      "null_pct": 0.99,
+      "null_med": 0.00262,
+      "null_p95": 0.40661,
+      "above_own_null_p95": true,
+      "argmax_off_px": 22.4,
+      "act_at_site": 0.2072,
+      "nearest_peak_px": 29.0,
+      "nearest_peak_score": 0.875
+    },
+    {
+      "city": "richmond",
+      "pano": "1759178911495771",
+      "x": 0.5197303339935238,
+      "y": 0.5219953845879944,
+      "field": "far",
+      "dist_m": 36.1,
+      "px": 21.7,
+      "group": "rated",
+      "verdict": "visible",
+      "act": 0.18581,
+      "null_pct": 0.855,
+      "null_med": 0.00299,
+      "null_p95": 0.45947,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.5,
+      "act_at_site": 0.03628,
+      "nearest_peak_px": 39.5,
+      "nearest_peak_score": 0.693
+    },
+    {
+      "city": "richmond",
+      "pano": "372149922554566",
+      "x": 0.03474748091264204,
+      "y": 0.5273737312085701,
+      "field": "far",
+      "dist_m": 29.0,
+      "px": 27.0,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.60787,
+      "null_pct": 0.935,
+      "null_med": 0.00026,
+      "null_p95": 0.66182,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.2,
+      "act_at_site": 0.08643,
+      "nearest_peak_px": 26.8,
+      "nearest_peak_score": 0.648
+    },
+    {
+      "city": "richmond",
+      "pano": "386363531219281",
+      "x": 0.4905050936612216,
+      "y": 0.5144444876006156,
+      "field": "far",
+      "dist_m": 55.1,
+      "px": 14.2,
+      "group": "rated",
+      "verdict": "visible",
+      "act": 0.05344,
+      "null_pct": 0.765,
+      "null_med": 0.00616,
+      "null_p95": 0.71793,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.5,
+      "act_at_site": 0.04411,
+      "nearest_peak_px": 51.3,
+      "nearest_peak_score": 0.847
+    },
+    {
+      "city": "richmond",
+      "pano": "3956582084664486",
+      "x": 0.46505054820667613,
+      "y": 0.5192929169625947,
+      "field": "far",
+      "dist_m": 41.2,
+      "px": 19.0,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.82894,
+      "null_pct": 0.985,
+      "null_med": 0.00038,
+      "null_p95": 0.42644,
+      "above_own_null_p95": true,
+      "argmax_off_px": 21.7,
+      "act_at_site": 0.33381,
+      "nearest_peak_px": 26.2,
+      "nearest_peak_score": 0.891
+    },
+    {
+      "city": "richmond",
+      "pano": "423989957171190",
+      "x": 0.42667711943494924,
+      "y": 0.5250655456146334,
+      "field": "far",
+      "dist_m": 31.7,
+      "px": 24.7,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.03764,
+      "null_pct": 0.855,
+      "null_med": 0.00513,
+      "null_p95": 0.85755,
+      "above_own_null_p95": false,
+      "argmax_off_px": 2.1,
+      "act_at_site": 0.03594,
+      "nearest_peak_px": 57.4,
+      "nearest_peak_score": 0.858
+    },
+    {
+      "city": "richmond",
+      "pano": "423989957171190",
+      "x": 0.5133691152905883,
+      "y": 0.5247863607532633,
+      "field": "far",
+      "dist_m": 32.0,
+      "px": 24.4,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.73132,
+      "null_pct": 0.9,
+      "null_med": 0.00532,
+      "null_p95": 0.85773,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.5,
+      "act_at_site": 0.11268,
+      "nearest_peak_px": 30.9,
+      "nearest_peak_score": 0.861
+    },
+    {
+      "city": "richmond",
+      "pano": "423989957171190",
+      "x": 0.5291509811918936,
+      "y": 0.5252428801194406,
+      "field": "far",
+      "dist_m": 31.5,
+      "px": 24.9,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.86011,
+      "null_pct": 1.0,
+      "null_med": 0.00649,
+      "null_p95": 0.85773,
+      "above_own_null_p95": true,
+      "argmax_off_px": 21.7,
+      "act_at_site": 0.29615,
+      "nearest_peak_px": 22.7,
+      "nearest_peak_score": 0.861
+    },
+    {
+      "city": "richmond",
+      "pano": "593421160518191",
+      "x": 0.7702458544241675,
+      "y": 0.5173858634506956,
+      "field": "far",
+      "dist_m": 45.7,
+      "px": 17.1,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.01255,
+      "null_pct": 0.115,
+      "null_med": 0.0757,
+      "null_p95": 0.90758,
+      "above_own_null_p95": false,
+      "argmax_off_px": 10.2,
+      "act_at_site": 0.00951,
+      "nearest_peak_px": 70.4,
+      "nearest_peak_score": 0.339
+    },
+    {
+      "city": "richmond",
+      "pano": "639645829096010",
+      "x": 0.23492423780036692,
+      "y": 0.5502974155481681,
+      "field": "near",
+      "dist_m": 15.7,
+      "px": 49.9,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.02843,
+      "null_pct": 0.475,
+      "null_med": 0.05058,
+      "null_p95": 0.84704,
+      "above_own_null_p95": false,
+      "argmax_off_px": 10.7,
+      "act_at_site": 0.02378,
+      "nearest_peak_px": 42.5,
+      "nearest_peak_score": 0.143
+    },
+    {
+      "city": "richmond",
+      "pano": "662385816800591",
+      "x": 0.44464645848129736,
+      "y": 0.5070707009055397,
+      "field": "far",
+      "dist_m": 112.5,
+      "px": 7.0,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.14685,
+      "null_pct": 0.88,
+      "null_med": 0.00949,
+      "null_p95": 0.60457,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.3,
+      "act_at_site": 0.00867,
+      "nearest_peak_px": 44.1,
+      "nearest_peak_score": 0.835
+    },
+    {
+      "city": "richmond",
+      "pano": "662385816800591",
+      "x": 0.5236363821318656,
+      "y": 0.5209091094045928,
+      "field": "far",
+      "dist_m": 38.0,
+      "px": 20.6,
+      "group": "witnessed",
+      "verdict": null,
+      "act": 0.42569,
+      "null_pct": 0.91,
+      "null_med": 0.01023,
+      "null_p95": 0.82634,
+      "above_own_null_p95": false,
+      "argmax_off_px": 22.4,
+      "act_at_site": 0.21966,
+      "nearest_peak_px": 36.0,
+      "nearest_peak_score": 0.605
+    },
+    {
+      "city": "richmond",
+      "pano": "723487737079243",
+      "x": 0.006868693033854166,
+      "y": 0.5435353966915246,
+      "field": "far",
+      "dist_m": 18.2,
+      "px": 43.1,
+      "group": "rated",
+      "verdict": "visible",
+      "act": 0.94587,
+      "null_pct": 0.965,
+      "null_med": 0.01808,
+      "null_p95": 0.945,
+      "above_own_null_p95": true,
+      "argmax_off_px": 7.4,
+      "act_at_site": 0.77932,
+      "nearest_peak_px": 117.4,
+      "nearest_peak_score": 0.945
+    }
+  ]
+}
\ No newline at end of file
diff --git a/docs/curb_ramp_data_sourcing.md b/docs/curb_ramp_data_sourcing.md
index 0289546..0bf3b76 100644
--- a/docs/curb_ramp_data_sourcing.md
+++ b/docs/curb_ramp_data_sourcing.md
@@ -98,6 +98,11 @@ benchmark keeps surfacing (Paterson's paired tactile surfaces, Gainesville's dia
 ramps) plausibly are fixable that way. Those are two different populations with two different
 programmes attached, and nobody had sized them.
 
+> **Since qualified by §0c.** The split below stands as a measurement, but its "fixable by"
+> column's hard binary does not: the model detects other far-field ramps of the *same apparent
+> size* as its silent misses at a median 57% rate, so far-field failure is graded sensitivity,
+> not a floor. Read the far/near boundary as a difficulty gradient, not a reachability partition.
+
 Script: `scripts/analysis/miss_decomposition.py` (15 tests). Reads the committed low-floor caches,
 so no GPU, no network, no imagery. Threshold 0.30 (the #79 recommendation); boundary 18 m, the last
 distance at which the model still has adequate signal.
@@ -287,8 +292,9 @@ what the gallery is for.
 
 - **`silent` is still an upper bound**, now with a floor under it. It means the cached detections
   witness nothing there. Occlusion, deep shadow, debris and GT disagreement all still live inside
-  the unwitnessed remainder, and separating them needs the imagery — that is #46's gallery half,
-  **the crops are built but the reviewer pass is not done**.
+  the unwitnessed remainder, and separating them needs the imagery — that is #46's gallery half.
+  **The reviewer pass is now done** (one rater, no second — `docs/replication.md` §"What the first
+  pass produced"), and its far-field verdicts raised their own question: **§0c**.
 - **The witness test is one-directional.** A witnessed ramp is confirmed recognizable; an
   unwitnessed one is not confirmed *un*recognizable, since every challenger is weaker than RampNet
   on this task and may simply have missed it too.
@@ -320,7 +326,10 @@ sourcing-addressable:
 | `unclear` | cannot tell from this imagery | excluded from every rate |
 
 **The `visible` rate over those 50, applied to the 59, is what converts the bracket into a point
-estimate.** Until it is run, quote the bracket.
+estimate.** It has been run (2026-07-31, one rater): near-field `visible` 7 of 13, which puts the
+sourcing-addressable population at **~0.013 recall points** (~19 chance-corrected witnessed + 7
+visible, against 2,060 pooled GT — `docs/replication.md`). Single-rater caveat applies, and the
+**far-field** verdicts from the same pass raised the question §0c takes up.
 - **Some of `merged` may be double-marked GT.** 24 of 124 pairs sit below 8 px (~25 cm at 10 m),
   which is not a physical spacing for two ramps; on the verdict splits that is plausibly one ramp
   marked twice. If so they are *spurious GT* and leave the population entirely rather than changing
@@ -330,6 +339,166 @@ estimate.** Until it is run, quote the bracket.
 - **`sub_threshold` is not free recall.** Those ramps are recoverable by lowering the threshold,
   which #54/#55 already evaluated and priced in precision; 0.30 was chosen knowing it.
 
+## 0c. The far-field `visible` anomaly: the pixel floor does not survive its own hits
+
+The reviewer pass produced a result §0a's framing did not predict. Of the **37 far-field**
+silent-miss crops: **34 `visible`, 2 `context-only`, 1 `unclear`** — a 94% visible rate over
+rateable crops, with **zero** `occluded` and **zero** `lighting` verdicts. Three facts sharpen it:
+
+- the rubric licenses `visible` only on the **model-resolution panel** (`benchmark/RUBRICS.md`),
+  so this is not the reviewer spending the 4× stored pixels the model never received;
+- every rated crop is **unwitnessed** — none of the 8 challenger models put anything in radius
+  either;
+- the deepest crops (40–150 m, down to **10.5 model px**) were rated visible **9 of 9**.
+
+At face value: ramps resolvable at the model's own pixel budget, invisible to all eight models —
+against the reading that far-field misses are pixel-starved and unreachable by any training-side
+fix. The four-hypothesis study design is on #46 (2026-07-31); this section is **Phase 0**: check
+the *sample* (the rated 37 passed two selection filters) and check the framing against the model's
+own far-field behaviour, before the verdicts are allowed to mean anything.
+
+Script: `scripts/analysis/farfield_forensics.py` (21 tests); result JSON
+`analysis_out/farfield_forensics.json`. Committed inputs only — the low-floor caches, the witness
+list, the gallery manifest and verdicts, and the imagery manifests' `width` fields. No GPU, no
+network, no imagery.
+
+### The sample: survivorship is real, mild, and now quantified
+
+The 83 far-field silent misses reduce to 37 rated through two filters — **witnessed** (37,
+already explained by another model's detection) and the **30-source-pixel judgeability floor**
+(9). The floor is not one floor: stored panoramas run 4096–16384 px wide while `geom()` sizes
+ramps at the model's 4096-px input, so 30 source px is a different model-pixel cut per split:
+
+| split | tier | stored px | floor (model px) | far-silent | unwitnessed | rated |
+| :--- | :--- | ---: | ---: | ---: | ---: | ---: |
+| richmond | mapillary | 4096–12288 | 10.0–30.0 | 13 | 5 | 4 |
+| bend | gsv | 13312–16384 | 7.5–9.2 | 14 | 9 | 9 |
+| clovis | mapillary | 5760 | 21.3 | 9 | 4 | 3 |
+| morgantown | mapillary | 4096 | 30.0 | 6 | 2 | 1 |
+| annapolis | mapillary | 8000 | 15.4 | 14 | 8 | 2 |
+| paterson | gsv | 16384 | 7.5 | 7 | 4 | 4 |
+| gainesville | gsv | 16384 | 7.5 | 20 | 14 | 14 |
+
+Which split a miss happened in decides whether a reviewer ever saw it — the 16384-px GSV splits
+admit far misses down to 7.5 model px while morgantown stops at 30, and the deck comes out
+**27 GSV / 10 Mapillary**.
+
+| population | n | dist q1/med/q3 (m) | px q1/med/q3 |
+| :--- | ---: | :---: | :---: |
+| rated (reached the deck) | 37 | 23.6 / 27.6 / 39.4 | 19.9 / 28.3 / 33.2 |
+| below the floor (excluded) | 9 | 55.4 / 116.7 / 150.0 | 5.2 / 6.7 / 14.1 |
+| witnessed (never queued) | 37 | 23.4 / 29.5 / 36.3 | 21.5 / 26.5 / 33.5 |
+| **all far-field silent misses** | **83** | 23.6 / 31.2 / 42.6 | 18.4 / 25.0 / 33.1 |
+| far-field hits, for contrast | 453 | 20.3 / 21.3 / 33.9 | 23.1 / 36.6 / 38.6 |
+
+- **AUC(rated px vs unrated far-silent px) = 0.600**; the rated median sits at the **62nd
+  percentile** of the far-silent size distribution. A bias toward bigger/closer exists and is mild.
+- The 9 excluded items are the **extreme tail** — median 117 m, three of them above-horizon clamps
+  (i.e. not distances at all). So the 94% generalizes to the far-silent *core* (~18–50 m); it says
+  nothing about the deep tail, which is exactly where pixel starvation is most plausible.
+- Mis-binning guards: **zero** above-horizon clamps among the rated 37, and the deck is majority
+  GSV — the tier where flat-ground distance is trustworthy (Spearman 0.95 vs 0.81).
+
+### The framing: the model's own hits refute a hard pixel floor
+
+The decisive check needs no reviewer at all. If far-field silence were pixel-starvation, the model
+should not be detecting *other* ramps at the same apparent size. It is:
+
+| band | GT | recall | silent misses | rated | rated `visible` |
+| :--- | ---: | ---: | ---: | ---: | ---: |
+| 18–25 m | 395 | 0.777 | 25 | 14 | 12 |
+| 25–40 m | 226 | 0.549 | 32 | 14 | 13 |
+| 40–150 m | 72 | 0.292 | 22 | 9 | 9 |
+| clamp ≥ 150 m | 5 | 0.200 | 3 | 0 | 0 |
+
+- **Matched-size detection rate**: for each `visible` miss, the model's recall over all far-field
+  GT within ±20% of that miss's apparent size is **median 0.57** (q1 0.31, q3 0.74). A hard pixel
+  floor would put these near zero.
+- **AUC(far-hit px vs far-silent px) = 0.718** — size matters, but it is far from deciding.
+- Recall declines **0.777 → 0.549 → 0.292** across the bands. Even at 40–150 m the model finds
+  roughly 3 in 10 (and the pooled 25–40 m rate agrees with E1's gold-set 0.49 at the same range).
+
+**Far-field failure is graded sensitivity, not a cliff.** A silent far-field miss is not a ramp
+below a physical detection floor — it is the unlucky tail of a process that succeeds on most
+same-sized ramps. That is consistent with `docs/detection_recall_analysis.md`'s sensitivity
+finding and with the human verdicts, and inconsistent with reading "more examples do not add
+pixels" as a claim about *reachability*. (As a claim about pixels it remains true; the error was
+inferring unreachability from it.)
+
+### Phase 1: attenuated or absent? Almost never absent
+
+`silent` is a statement about **peaks** — no `peak_local_max` peak ≥ 0.05 within the match radius.
+Phase 1 makes the statement about the **heatmap**: `scripts/analysis/silent_activation.py`
+(14 tests) loads the published checkpoint (`projectsidewalk/rampnet-model` — the weights every
+committed cache came from), runs one pass per panorama holding a silent miss (single-pass fp32,
+matching `op_cache`), and reads the max heatmap value inside the match radius. The scaled matcher
+space *is* the 512×1024 heatmap grid, so the window is exactly the matcher's. Result JSON:
+`analysis_out/silent_activation.json`; run on the local RTX 3070, all 128 pooled silent misses.
+
+| population | n | act q1 / med / q3 | act ≥ 0.01 |
+| :--- | ---: | :---: | ---: |
+| near / rated | 13 | 0.009 / 0.099 / 0.197 | 9 |
+| near / witnessed | 32 | 0.033 / 0.211 / 0.592 | 30 |
+| far / rated | 37 | 0.022 / 0.076 / 0.409 | 34 |
+| far / below-floor | 9 | 0.042 / 0.194 / 0.381 | 8 |
+| far / witnessed | 37 | 0.045 / 0.188 / 0.615 | 37 |
+| **all silent misses** | **128** | 0.032 / 0.136 / 0.548 | **118** |
+
+What that in-window mass *is* (classes are act ranges; the offset and nearest-peak columns
+confirm the intended reading rather than define it):
+
+| class | definition | n | near / far | rated `visible` | argmax offset med | nearest floor peak med |
+| :--- | :--- | ---: | :---: | ---: | ---: | ---: |
+| **absent** | act < 0.01 | **10** | 6 / 4 | 5 | 22.0 px | 77.5 px (3.4 R) |
+| **faint local** | 0.01 ≤ act < 0.05 | 39 | 12 / 27 | 13 | **10.2 px** | 85.6 px (3.8 R) |
+| **tail** | act ≥ 0.05 | 79 | 27 / 52 | 23 | 22.3 px | **31.1 px (1.4 R)** |
+
+- **Only 10 of 128 silent misses (8%) have a genuinely flat heatmap.** "Silent = the model saw
+  nothing" is wrong for 92% of the bucket; `silent` was peak bookkeeping, not absence of response.
+- **62% are a neighbouring mode's tail.** The argmax sits in the window's outer quarter in 75 of
+  79, and the nearest cached floor peak is ~1.4 R away with **median score 0.685** — a *confident*
+  adjacent detection (70/79 within 2 R). That mode is a neighbour ramp's TP, an FP, or plausibly
+  this very ramp localized just outside the radius — the `localization` bucket only inspects
+  *kept* (≥ 0.30) annulus peaks, so a floor-level one leaves a miss "silent". Whichever it is,
+  this is the σ/representation family again (`merged`'s mechanism), not vocabulary.
+- **30% are a faint local response at the site itself** (mass on-site in 30 of 39, nothing else
+  within ~3.8 R) — the `sub_threshold` continuum extending below the floor. Attenuation, not
+  blindness.
+- For the far-field rated-`visible` population — the anomaly itself — the split is **3 absent /
+  12 faint-local / 19 tail**: the model is responding at or next to ~91% of the far ramps a human
+  called resolvable. Consistent with Phase 0's graded-sensitivity reading; squarely against a
+  vocabulary hole.
+- The strict per-pano null (azimuth-randomized at the site's elevation, self-excluding within 2 R)
+  passes 31/128 at its p95 — a deliberately hard bar, since the p95 is set by the pano's strongest
+  modes; the decomposition above is the sharper lens.
+
+### What changes, what does not, and what is still open
+
+- **§0a's measured split stands** (247 far / 180 near at 18 m). What falls is the hard binary in
+  its "fixable by" column: the far field is *harder*, not *unreachable*.
+- **The sourcing bracket (§0b) excluded all 83 far-field silent misses from the addressable
+  population because of that binary.** That exclusion is no longer safe — but Phase 1 cuts the
+  other way too: of the 45 *near-field* silent misses the 0.013 estimate rests on, only **6 are
+  heatmap-absent**; the rest are faint-local (12) or an adjacent confident mode (27), i.e. the
+  calibration and σ families §0b already prices separately. The 0.013 point estimate is
+  deliberately **not revised** in either direction until Phase 2 (the scale counterfactual, whose
+  primary target is now the 10 absent sites plus whether scale lifts faint-local over the floor)
+  and Phase 3 (the decoy control on the verdicts) run. Quote 0.013 with this section attached.
+- **Multi-view's remedy logic is untouched** — a ramp invisible at 30 m is at 8 m two panoramas
+  later whatever the failure mechanism — but §0a's "MV ceiling" column shares the binary
+  assumption and will move with the same phases.
+- **The human-side caveat is live.** One rater; and the 9-of-9 `visible` rate in the deepest band
+  (down to 10.5 model px) is where pointed-verification bias would show most strongly. Phase 3's
+  decoy deck should therefore be **stratified by distance band**, oversampling 40–150 m.
+
+**The takeaway.** "Are far ramps harder?" — yes, threefold (recall 0.777 → 0.292 across the
+bands), but Phases 0–1 show distance acting as a **stressor on failure families this taxonomy
+already prices, not as a new category of failure**: 62% the σ/representation family, 30% the
+`sub_threshold` continuum, 8% genuine absence. The implied lever is therefore decoder- and
+representation-side — target σ, peak spacing, threshold calibration, and Phase 2's scale question
+for the residual — **not far-field training vocabulary**; and multi-view remains the one remedy
+that sidesteps all three mechanisms at once, by re-presenting the same ramp near-field.
+
 ## 1. The current training corpus is mostly one city
 
 Stage 1 is built from three cities' open-government inventories (`docs/data_provenance.md` §1).
diff --git a/scripts/analysis/farfield_forensics.py b/scripts/analysis/farfield_forensics.py
new file mode 100644
index 0000000..7b843da
--- /dev/null
+++ b/scripts/analysis/farfield_forensics.py
@@ -0,0 +1,382 @@
+"""Phase 0 of the far-field `visible` anomaly study: is the rated sample representative? (#46)
+
+The reviewer pass over the silent misses (``benchmark/miss_taxonomy_46/silent__jonf.json``)
+rated **34 of 36** rateable far-field crops ``visible`` — the ramp's own pixels present
+and carrying its appearance *in the model-resolution panel*. At face value that
+contradicts the pixel-starvation framing E1 attached to the far field
+(``docs/curb_ramp_data_sourcing.md`` §0a: "a 1.2 m ramp at 30 m is ~25 px — more
+examples do not add pixels"), which is the assumption the 18 m far/near split — and
+through it the sourcing bracket and the multi-view sizing — stands on.
+
+Before that contradiction is allowed to mean anything, the sample has to be checked
+(#46, hypothesis H1): the 37 rated far-field crops are not a random draw from the 83
+far-field silent misses. They passed two filters — **unwitnessed** (no other model
+detected anything there either) and the **30-source-pixel judgeability floor** — and
+the floor bites at a different apparent size on every split, because the stored
+panoramas range from 4096 to 16384 px wide while ``geom()`` sizes ramps at the model's
+4096-px input. On a 16384-px split the floor admits ramps down to **7.5 model px**; on
+morgantown it stops at **30**. If the rated crops are the biggest and closest of the
+far field, a 94% visible rate there says little about the population.
+
+This measures that, from committed data alone:
+
+* **the two filters' effect on the sample**, split by split — which populations could
+  even reach the deck, and where the rated 37 sit in the far-silent size distribution;
+* **whether apparent size discriminates far-field hits from far-field silent misses**
+  at all — if the model detects other ramps of the *same* apparent size at a healthy
+  rate, a hard pixel floor cannot be what makes these particular ramps silent;
+* **the mis-binning guards**: above-horizon clamps (``geom()`` sends y <= 0.5 straight
+  to 150 m) and the GSV/Mapillary tier split, since the flat-ground distance estimate
+  is weaker on Mapillary rigs (Spearman 0.81 vs 0.95, §0a).
+
+Everything is read from committed files (``analysis_out/op_cache``,
+``analysis_out/silent_witness.json``, ``benchmark/miss_taxonomy_46/``,
+``benchmark/<city>/imagery_manifest.json``): no GPU, no network, no imagery.
+
+    python scripts/analysis/farfield_forensics.py
+    python scripts/analysis/farfield_forensics.py --json-out analysis_out/farfield_forensics.json
+"""
+import argparse
+import json
+import os
+import sys
+
+REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+OUT = os.environ.get("RAMPNET_ANALYSIS_OUT", os.path.join(REPO, "analysis_out"))
+sys.path.insert(0, REPO)
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+import miss_taxonomy as mt  # noqa: E402
+from miss_decomposition import (  # noqa: E402
+    DEFAULT_THRESHOLD, FAR_BOUNDARY_M, TIER, US_SPLITS)
+from miss_gallery import (  # noqa: E402
+    JUDGEABLE_SOURCE_PX, MODEL_WIDTH, load_queue, source_px, tag_key)
+
+# Distance bands inside the far field, in metres. The first two match E1's gold-set
+# bins (recall 0.90 at 18-25, 0.49 at 25-40), so the pooled-benchmark rates here are
+# directly comparable to the numbers the pixel-starvation framing was built on.
+# ``geom()`` clamps above-horizon points to 150 m, so the clamp is its own band —
+# those distances are not measurements, they are the estimator giving up.
+FAR_BANDS = ((18.0, 25.0), (25.0, 40.0), (40.0, 150.0))
+
+# Tolerance for "same apparent size" when asking how often the model detects OTHER
+# far-field ramps of a rated miss's size. +/-20% of the target px is ~ +/-20% of
+# distance (px is 1/d), comfortably inside one of E1's bins.
+MATCH_TOL = 0.20
+
+
+# --------------------------------------------------------------------------- #
+# Pure core (no I/O) — unit-tested in tests/test_farfield_forensics.py
+# --------------------------------------------------------------------------- #
+def auc(a, b):
+    """P(a random draw from ``a`` exceeds one from ``b``), ties counting half.
+
+    The Mann-Whitney statistic scaled to [0, 1]: 0.5 means the two samples are
+    indistinguishable on this variable, 1.0 means every ``a`` exceeds every ``b``.
+    O(n*m); the populations here are dozens-to-hundreds, so clarity wins.
+    """
+    if not a or not b:
+        return float("nan")
+    wins = ties = 0
+    for x in a:
+        for y in b:
+            if x > y:
+                wins += 1
+            elif x == y:
+                ties += 1
+    return (wins + 0.5 * ties) / (len(a) * len(b))
+
+
+def quartiles(values):
+    """``(q1, median, q3)`` by simple index — enough for reporting, no interpolation."""
+    if not values:
+        return (float("nan"),) * 3
+    v = sorted(values)
+    n = len(v)
+    return v[n // 4], v[n // 2], v[(3 * n) // 4]
+
+
+def effective_floor_model_px(source_width, floor=JUDGEABLE_SOURCE_PX,
+                             model_width=MODEL_WIDTH):
+    """The judgeability floor translated into MODEL pixels for one stored width.
+
+    ``source_px = model_px * source_width / model_width``, so a floor fixed in
+    source pixels admits smaller model-pixel ramps the wider the stored pano is.
+    This asymmetry is what shapes the rated deck's composition across splits.
+    """
+    return floor * model_width / source_width
+
+
+def band_of(row, bands=FAR_BANDS):
+    """The far band a row falls in, ``'clamp'`` for above-horizon rows, else None.
+
+    The clamp test is on ``y``, mirroring ``miss_decomposition.above_horizon`` —
+    a ground ramp cannot sit at or above the horizon, so its 150 m is an artifact
+    of an unleveled rig or a hill, not a distance.
+    """
+    if row["y"] <= 0.5:
+        return "clamp"
+    for lo, hi in bands:
+        if lo <= row["dist"] < hi:
+            return (lo, hi)
+    return None
+
+
+def matched_rate(rows, px, tol=MATCH_TOL):
+    """``(hits, n)`` among ``rows`` whose apparent size is within ``tol`` of ``px``.
+
+    The counterfactual the floor question needs: of every far-field GT ramp the
+    benchmark scores at (about) this apparent size, how many did the model find?
+    """
+    sel = [r for r in rows if abs(r["px"] - px) <= tol * px]
+    return sum(1 for r in sel if r["hit"]), len(sel)
+
+
+def percentile_rank(population, x):
+    """Fraction of ``population`` strictly below ``x``, ties counting half."""
+    if not population:
+        return float("nan")
+    below = sum(1 for v in population if v < x)
+    ties = sum(1 for v in population if v == x)
+    return (below + 0.5 * ties) / len(population)
+
+
+def row_key(row):
+    """The identity a row shares with the witness list and the gallery manifest."""
+    return (row["city"], row["pano"], round(float(row["x"]), 6),
+            round(float(row["y"]), 6))
+
+
+# --------------------------------------------------------------------------- #
+# I/O
+# --------------------------------------------------------------------------- #
+def stored_widths(city):
+    """``{pano_id: width}`` from the committed imagery manifest, or ``{}``.
+
+    The manifest was committed to pin the imagery by content hash (#94); its
+    ``width`` field is what lets this script translate the source-pixel floor
+    into model pixels without touching a single image.
+    """
+    path = os.path.join(REPO, "benchmark", city, "imagery_manifest.json")
+    if not os.path.exists(path):
+        return {}
+    with open(path, encoding="utf-8") as fh:
+        payload = json.load(fh)
+    return {pid: rec["width"] for pid, rec in payload.get("panos", {}).items()
+            if rec.get("width")}
+
+
+def load_rated(gallery_dir, field="far"):
+    """The reviewer's rated items: manifest entry + verdict, keyed like the tags.
+
+    ``field`` restricts to one distance population; ``None`` returns all 50
+    (Phase 1's activation forensics wants the near-field verdicts too).
+    """
+    with open(os.path.join(gallery_dir, "silent_gallery", "manifest.json"),
+              encoding="utf-8") as fh:
+        manifest = json.load(fh)
+    with open(os.path.join(gallery_dir, "silent__jonf.json"), encoding="utf-8") as fh:
+        verdicts = json.load(fh)["verdicts"]
+    out = {}
+    for key, item in manifest["items"].items():
+        if field is not None and item.get("field") != field:
+            continue
+        v = verdicts.get(key)
+        out[key] = {**item, "verdict": v if isinstance(v, str)
+                    else (v or {}).get("verdict")}
+    return out
+
+
+def main(argv=None):
+    p = argparse.ArgumentParser(description=__doc__.split("\n")[0])
+    p.add_argument("--threshold", type=float, default=DEFAULT_THRESHOLD)
+    p.add_argument("--witness", default=os.path.join(OUT, "silent_witness.json"))
+    p.add_argument("--gallery", default=os.path.join(REPO, "benchmark",
+                                                     "miss_taxonomy_46"))
+    p.add_argument("--json-out", default=None)
+    args = p.parse_args(argv)
+
+    # Populations, all at the deployed threshold. --------------------------------
+    pooled = []
+    for city in US_SPLITS:
+        loaded = mt.load_rows(city, args.threshold, rng=None)
+        if loaded is not None:
+            pooled.extend(loaded[0])
+    far = [r for r in pooled if r["field"] == "far"]
+    far_hits = [r for r in far if r["hit"]]
+    far_silent = [r for r in far if not r["hit"] and r["bucket"] == "silent"]
+
+    queue = load_queue(args.witness)
+    unw_far = [r for r in far_silent if row_key(r) in queue]
+
+    rated = load_rated(args.gallery)
+    rated_keys = {(v["city"], v["pano"], round(float(v["x"]), 6),
+                   round(float(v["y"]), 6)) for v in rated.values()}
+    rated_rows = [r for r in unw_far if row_key(r) in rated_keys]
+    excluded_rows = [r for r in unw_far if row_key(r) not in rated_keys]
+
+    widths = {city: stored_widths(city) for city in US_SPLITS}
+    visible = [v for v in rated.values() if v["verdict"] == "visible"]
+
+    print(f"=== Far-field 'visible' anomaly, Phase 0: sample forensics "
+          f"(threshold {args.threshold}, boundary {FAR_BOUNDARY_M:.0f} m, #46) ===\n")
+    print(f"far-field GT {len(far)}, hits {len(far_hits)} "
+          f"(recall {len(far_hits)/len(far):.3f}), silent misses {len(far_silent)}; "
+          f"unwitnessed {len(unw_far)}, rated {len(rated_rows)}, "
+          f"below the floor {len(excluded_rows)}")
+    tally = {}
+    for v in rated.values():
+        tally[v["verdict"]] = tally.get(v["verdict"], 0) + 1
+    print(f"reviewer verdicts over the rated set: " +
+          ", ".join(f"{k} {n}" for k, n in sorted(tally.items(),
+                                                  key=lambda kv: -kv[1])))
+
+    # 1. The floor, split by split. ----------------------------------------------
+    print(f"\n{'-'*78}\n1. THE FLOOR IS NOT ONE FLOOR — {JUDGEABLE_SOURCE_PX:.0f} "
+          f"source px in model pixels, per split\n{'-'*78}")
+    print(f"{'split':>12} {'tier':>10} {'stored px':>12} {'floor(model px)':>16} "
+          f"{'far-silent':>11} {'unwitn.':>8} {'rated':>6}")
+    per_split = {}
+    for city in US_SPLITS:
+        fs = [r for r in far_silent if r["city"] == city]
+        if not fs:
+            continue
+        ws = sorted({widths[city].get(r["pano"]) for r in fs
+                     if widths[city].get(r["pano"])})
+        w_lo, w_hi = (ws[0], ws[-1]) if ws else (None, None)
+        floor_lo = effective_floor_model_px(w_hi) if w_hi else float("nan")
+        floor_hi = effective_floor_model_px(w_lo) if w_lo else float("nan")
+        floor_s = (f"{floor_lo:.1f}" if w_lo == w_hi else
+                   f"{floor_lo:.1f}-{floor_hi:.1f}")
+        n_unw = sum(1 for r in unw_far if r["city"] == city)
+        n_rated = sum(1 for r in rated_rows if r["city"] == city)
+        stored_s = (f"{w_lo}" if w_lo == w_hi else f"{w_lo}-{w_hi}") if ws else "?"
+        print(f"{city:>12} {TIER.get(city, '-'):>10} {stored_s:>12} {floor_s:>16} "
+              f"{len(fs):>11} {n_unw:>8} {n_rated:>6}")
+        per_split[city] = {"far_silent": len(fs), "unwitnessed": n_unw,
+                           "rated": n_rated, "stored_width_min": w_lo,
+                           "stored_width_max": w_hi}
+    print("\n  The deck's composition follows the floor: the 16384-px splits admit")
+    print("  far misses down to 7.5 model px, morgantown stops at 30. Which split a")
+    print("  miss happened in decides whether a reviewer ever saw it.")
+
+    # 2. Where the rated set sits in the far-silent population. -------------------
+    print(f"\n{'-'*78}\n2. SURVIVORSHIP — where the rated {len(rated_rows)} sit "
+          f"among all {len(far_silent)} far-field silent misses\n{'-'*78}")
+    print(f"{'population':>34} {'n':>4} {'dist q1/med/q3 (m)':>20} "
+          f"{'px q1/med/q3':>15}")
+    pops = {
+        "rated (reached the deck)": rated_rows,
+        "below the floor (excluded)": excluded_rows,
+        "witnessed (never queued)": [r for r in far_silent
+                                     if row_key(r) not in queue],
+        "ALL far-field silent misses": far_silent,
+        "far-field hits (for contrast)": far_hits,
+    }
+    stats = {}
+    for name, rows in pops.items():
+        dq = quartiles([r["dist"] for r in rows])
+        pq = quartiles([r["px"] for r in rows])
+        stats[name] = {"n": len(rows), "dist_q": dq, "px_q": pq}
+        print(f"{name:>34} {len(rows):>4} "
+              f"{dq[0]:>6.1f}/{dq[1]:>5.1f}/{dq[2]:>5.1f} "
+              f"{pq[0]:>5.1f}/{pq[1]:>4.1f}/{pq[2]:>4.1f}")
+    auc_rated = auc([r["px"] for r in rated_rows],
+                    [r["px"] for r in far_silent
+                     if row_key(r) not in rated_keys])
+    med_rank = percentile_rank([r["px"] for r in far_silent],
+                               quartiles([r["px"] for r in rated_rows])[1])
+    print(f"\n  AUC(rated px vs unrated far-silent px) = {auc_rated:.3f} "
+          f"(0.5 = no size bias)")
+    print(f"  the rated median px sits at the {med_rank:.0%} percentile of the "
+          f"far-silent population")
+
+    # 3. Does apparent size even separate hit from silent out here? ---------------
+    print(f"\n{'-'*78}\n3. DISCRIMINATION — is a far-field silent miss the size "
+          f"the model cannot see?\n{'-'*78}")
+    auc_hit = auc([r["px"] for r in far_hits], [r["px"] for r in far_silent])
+    print(f"  AUC(far-hit px vs far-silent px) = {auc_hit:.3f} "
+          f"(1.0 would mean size alone decides)\n")
+    print(f"{'band':>12} {'GT':>6} {'recall':>8} {'silent':>8} {'rated':>6} "
+          f"{'visible':>8}")
+    band_rows = {}
+    key_of = {tag_key(v["pano"], v["x"], v["y"]): v for v in rated.values()}
+    for band in list(FAR_BANDS) + ["clamp"]:
+        sel = [r for r in far if band_of(r) == band]
+        if not sel:
+            continue
+        n_hit = sum(1 for r in sel if r["hit"])
+        n_sil = sum(1 for r in sel if not r["hit"] and r["bucket"] == "silent")
+        n_rated = sum(1 for r in rated_rows if band_of(r) == band)
+        n_vis = sum(1 for r in rated_rows if band_of(r) == band
+                    and key_of.get(tag_key(r["pano"], r["x"], r["y"]),
+                                   {}).get("verdict") == "visible")
+        label = "clamp>=150" if band == "clamp" else f"{band[0]:.0f}-{band[1]:.0f} m"
+        print(f"{label:>12} {len(sel):>6} {n_hit/len(sel):>8.3f} {n_sil:>8} "
+              f"{n_rated:>6} {n_vis:>8}")
+        band_rows[label] = {"n_gt": len(sel), "recall": n_hit / len(sel),
+                            "silent": n_sil, "rated": n_rated, "visible": n_vis}
+
+    rates = []
+    for v in visible:
+        h, n = matched_rate(far, v["model_px"])
+        if n:
+            rates.append(h / n)
+    rq = quartiles(rates)
+    print(f"\n  MATCHED-SIZE DETECTION RATE: for each of the {len(visible)} "
+          f"'visible' misses, the model's")
+    print(f"  recall over ALL far-field GT within ±{MATCH_TOL:.0%} of that miss's "
+          f"apparent size:")
+    print(f"    q1/median/q3 = {rq[0]:.2f} / {rq[1]:.2f} / {rq[2]:.2f}")
+    print(f"  A hard pixel floor would put these near zero. The model routinely")
+    print(f"  detects other ramps of the same apparent size; silence at that size is")
+    print(f"  therefore not size-fated, and 'pixel-starved' is not a sufficient")
+    print(f"  explanation for these misses.")
+
+    # 4. Mis-binning guards. ------------------------------------------------------
+    print(f"\n{'-'*78}\n4. GUARDS — how much of this could the distance estimator "
+          f"be inventing?\n{'-'*78}")
+    n_clamp = {name: sum(1 for r in rows if r["y"] <= 0.5)
+               for name, rows in pops.items()}
+    print(f"  above-horizon clamps: rated {n_clamp['rated (reached the deck)']}, "
+          f"excluded {n_clamp['below the floor (excluded)']}, "
+          f"all far-silent {n_clamp['ALL far-field silent misses']}")
+    tiers = {}
+    for r in rated_rows:
+        tiers[TIER.get(r["city"], "-")] = tiers.get(TIER.get(r["city"], "-"), 0) + 1
+    print(f"  rated set by tier: " +
+          ", ".join(f"{k} {n}" for k, n in sorted(tiers.items())) +
+          "  (flat-ground distance is Spearman 0.95 on gsv, 0.81 on mapillary)")
+
+    if args.json_out:
+        os.makedirs(os.path.dirname(os.path.abspath(args.json_out)), exist_ok=True)
+        payload = {
+            "threshold": args.threshold, "boundary_m": FAR_BOUNDARY_M,
+            "judgeable_source_px": JUDGEABLE_SOURCE_PX,
+            "match_tolerance": MATCH_TOL,
+            "counts": {"far_gt": len(far), "far_hits": len(far_hits),
+                       "far_silent": len(far_silent),
+                       "unwitnessed_far": len(unw_far),
+                       "rated": len(rated_rows),
+                       "below_floor": len(excluded_rows)},
+            "verdicts": tally,
+            "per_split": per_split,
+            "populations": {name: {"n": s["n"], "dist_q1_med_q3": s["dist_q"],
+                                   "px_q1_med_q3": s["px_q"]}
+                            for name, s in stats.items()},
+            "auc_rated_vs_unrated_px": auc_rated,
+            "rated_median_px_percentile": med_rank,
+            "auc_hit_vs_silent_px": auc_hit,
+            "bands": band_rows,
+            "matched_size_detection_rate_q1_med_q3": rq,
+            "above_horizon": n_clamp,
+            "rated_by_tier": tiers,
+        }
+        with open(args.json_out, "w", encoding="utf-8") as fh:
+            json.dump(payload, fh, indent=2)
+        print(f"\nWrote {args.json_out}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/analysis/silent_activation.py b/scripts/analysis/silent_activation.py
new file mode 100644
index 0000000..be873ff
--- /dev/null
+++ b/scripts/analysis/silent_activation.py
@@ -0,0 +1,342 @@
+"""Phase 1 of the far-field `visible` anomaly study: attenuated, or absent? (#46)
+
+The ``silent`` bucket is defined by peak extraction: no ``peak_local_max`` peak at or
+above the 0.05 score floor within the match radius. That is a statement about *peaks*,
+not about the heatmap — 0.04 and 0.0001 are both "silent", and they are different
+failures. If the model produces a real-but-faint localized response at these ramps,
+the silent bucket is the tail of the same confidence continuum ``sub_threshold``
+lives on (a calibration/threshold/training story, gainesville's mechanism). If the
+heatmap is flat at chance level, the model has no representation of the ramp at all —
+a genuine vocabulary or scale gap, which is what Phase 2's scale counterfactual then
+separates.
+
+For every pooled silent miss (near and far — the near-field verdicts feed the 0.013
+sourcing estimate just as directly), this loads the published model, runs one forward
+pass per panorama, and reads:
+
+* ``act`` — the max heatmap value within the match radius of the missed ramp (the
+  radius and grid are exactly the matcher's: the scaled space *is* the 512x1024
+  heatmap). Note ``act`` can exceed the 0.05 floor without contradicting ``silent``
+  — a shoulder of a neighbouring peak is not a local maximum; those cases are
+  counted separately rather than silently pooled.
+* a per-miss **null**: the same radius-max at ``NULL_TRIALS`` random azimuths in the
+  same panorama at the same elevation — the same null shape every #46 analysis uses,
+  because both ramps and heatmap mass crowd the horizon band. ``act`` is reported as
+  a percentile of its own panorama's null, so "there is signal here" is a claim
+  against that pano's actual noise floor, not against zero.
+
+Model: the published ``projectsidewalk/rampnet-model`` weights — the same checkpoint
+every committed cache came from (``operating_point_curve.py`` extract). Single-pass,
+no TTA, fp32, matching ``analysis_out/op_cache``. Needs the panorama imagery
+(``benchmark/<city>/panos``, git-ignored — ``--panos-root`` from a worktree) and a
+GPU-ish machine; the RTX 3070 does ~10 s/pano.
+
+    python scripts/analysis/silent_activation.py --panos-root D:/Git/RampNet
+    python scripts/analysis/silent_activation.py --json-out analysis_out/silent_activation.json
+"""
+import argparse
+import json
+import os
+import random
+import sys
+
+REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+OUT = os.environ.get("RAMPNET_ANALYSIS_OUT", os.path.join(REPO, "analysis_out"))
+sys.path.insert(0, REPO)
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+import miss_taxonomy as mt  # noqa: E402
+from miss_decomposition import DEFAULT_THRESHOLD, US_SPLITS  # noqa: E402
+from farfield_forensics import load_rated, quartiles, row_key  # noqa: E402
+from rampnet.detection_eval import (  # noqa: E402
+    PANO_SCALE_X, PANO_SCALE_Y, radius_sq_for)
+
+NULL_TRIALS = 200
+NULL_SEED = 20260731
+
+# The heatmap grid IS the matcher's scaled space (PANO_SCALE_X x PANO_SCALE_Y =
+# 1024 x 512), asserted at runtime so a future resolution change cannot silently
+# desynchronize the two.
+HEAT_W, HEAT_H = int(PANO_SCALE_X), int(PANO_SCALE_Y)
+
+
+# --------------------------------------------------------------------------- #
+# Pure core (no torch, no I/O) — unit-tested in tests/test_silent_activation.py
+# --------------------------------------------------------------------------- #
+def radius_max(heat, x, y, radius_sq=None):
+    """Max heatmap value within the match radius of normalized point ``(x, y)``.
+
+    Columns wrap at the 360-degree seam (a radius crossing x=0 continues at x=1);
+    rows clamp — there is nothing above the top of a panorama. Values are clipped
+    to [0, 1] exactly as ``peaks_to_dets`` clips before peak extraction, so an
+    ``act`` here and a peak score there are on the same scale.
+    """
+    return site_profile(heat, x, y, radius_sq)[0]
+
+
+def site_profile(heat, x, y, radius_sq=None):
+    """``(act, off_px, center)`` for the window around normalized ``(x, y)``.
+
+    ``off_px`` is how far from the site the in-window maximum sits, and ``center``
+    is the value at the site itself — together they separate a response *at* the
+    ramp from a neighbouring mode's tail reaching *into* the window, which ``act``
+    alone cannot do (and 62% of silent misses turn out to need the distinction).
+    """
+    if radius_sq is None:
+        radius_sq = radius_sq_for()
+    H, W = len(heat), len(heat[0])
+    r = radius_sq ** 0.5
+    cx, cy = x * W, y * H
+    best, off = 0.0, 0.0
+    for row in range(max(0, int(cy - r)), min(H, int(cy + r) + 2)):
+        dy2 = (row - cy) ** 2
+        if dy2 >= radius_sq:
+            continue
+        span = (radius_sq - dy2) ** 0.5
+        for col in range(int(cx - span), int(cx + span) + 2):
+            dx = col - cx
+            if dx * dx + dy2 >= radius_sq:
+                continue
+            v = min(float(heat[row][col % W]), 1.0)
+            if v > best:
+                best, off = v, (dx * dx + dy2) ** 0.5
+    center = min(float(heat[min(H - 1, max(0, round(cy)))]
+                       [round(cx) % W]), 1.0)
+    return best, off, max(center, 0.0)
+
+
+def nearest_peak(preds, x, y):
+    """``(dist_px, score)`` of the closest cached floor peak, in matcher units.
+
+    ``preds`` are the panorama's cached ``(x, y, score)`` floor peaks (>= 0.05).
+    For a silent miss any such peak is by definition OUTSIDE the match radius, so
+    this measures how far away the nearest thing the model actually said is —
+    the difference between "a neighbouring mode's tail reaches the site" (~1-2
+    radii) and "the nearest response is nowhere near" (many radii).
+    """
+    if not preds:
+        return float("inf"), None
+    best, score = float("inf"), None
+    for p in preds:
+        dx = abs(p[0] - x) * PANO_SCALE_X
+        dx = min(dx, PANO_SCALE_X - dx)
+        d = (dx * dx + ((p[1] - y) * PANO_SCALE_Y) ** 2) ** 0.5
+        if d < best:
+            best, score = d, p[2]
+    return best, score
+
+
+def null_percentile(heat, x, y, rng, trials=NULL_TRIALS, radius_sq=None):
+    """``(act, percentile, null_med, null_p95)`` of the site's radius-max vs its pano.
+
+    The null keeps the ramp's elevation and randomizes azimuth — the shape every
+    other #46 null uses. Draws whose window would overlap the site's own window
+    (wrapped column distance under 2R) are rejected and redrawn: the question is
+    whether the site's response exceeds what the *rest* of the elevation band
+    produces, and a draw that reads the site's own bump back would contaminate
+    exactly the sparse-heatmap case this analysis exists to detect. The percentile
+    counts ties as half, so a flat heatmap reads 0.5, not 1.0.
+    """
+    if radius_sq is None:
+        radius_sq = radius_sq_for()
+    act = radius_max(heat, x, y, radius_sq)
+    W = len(heat[0])
+    exclude = 2.0 * (radius_sq ** 0.5) / W  # normalized column distance
+    draws = []
+    while len(draws) < trials:
+        nx = rng.random()
+        dx = abs(nx - x)
+        if min(dx, 1.0 - dx) < exclude:
+            continue
+        draws.append(radius_max(heat, nx, y, radius_sq))
+    draws.sort()
+    below = sum(1 for d in draws if d < act)
+    ties = sum(1 for d in draws if d == act)
+    pct = (below + 0.5 * ties) / trials
+    return act, pct, draws[trials // 2], draws[int(trials * 0.95)]
+
+
+def group_of(row, queue_keys, rated_by_rowkey):
+    """Which selection stratum a silent miss belongs to (Phase 0's partition)."""
+    key = row_key(row)
+    if key not in queue_keys:
+        return "witnessed"
+    if key in rated_by_rowkey:
+        return "rated"
+    return "below_floor"
+
+
+# --------------------------------------------------------------------------- #
+# I/O
+# --------------------------------------------------------------------------- #
+def main(argv=None):
+    p = argparse.ArgumentParser(description=__doc__.split("\n")[0])
+    p.add_argument("--threshold", type=float, default=DEFAULT_THRESHOLD)
+    p.add_argument("--witness", default=os.path.join(OUT, "silent_witness.json"))
+    p.add_argument("--gallery", default=os.path.join(REPO, "benchmark",
+                                                     "miss_taxonomy_46"))
+    p.add_argument("--panos-root", default=REPO,
+                   help="Checkout holding benchmark/<city>/panos (git-ignored, so "
+                        "in a worktree it lives in the main checkout instead).")
+    p.add_argument("--cities", default=",".join(US_SPLITS))
+    p.add_argument("--limit", type=int, default=None,
+                   help="Stop after this many panos (smoke test).")
+    p.add_argument("--json-out", default=None)
+    args = p.parse_args(argv)
+
+    import torch
+    import threshold_sweep as ts
+    assert (HEAT_W, HEAT_H) == (1024, 512), "heatmap grid != matcher space"
+
+    from miss_gallery import load_queue, pano_path
+    queue_keys = load_queue(args.witness)
+    rated = load_rated(args.gallery, field=None)
+    rated_by_rowkey = {(v["city"], v["pano"], round(float(v["x"]), 6),
+                        round(float(v["y"]), 6)): v for v in rated.values()}
+
+    from operating_point_curve import CACHE_DIR, read_cache
+    cities = [c.strip() for c in args.cities.split(",") if c.strip()]
+    by_pano, preds_by = {}, {}
+    for city in cities:
+        loaded = mt.load_rows(city, args.threshold, rng=None)
+        if loaded is None:
+            continue
+        for r in loaded[0]:
+            if not r["hit"] and r["bucket"] == "silent":
+                by_pano.setdefault((city, r["pano"]), []).append(r)
+        panos, _ = read_cache(os.path.join(CACHE_DIR, f"{city}.json"))
+        for pd in panos:
+            preds_by[(city, pd["pano"])] = pd["preds"]
+    n_miss = sum(len(v) for v in by_pano.values())
+    print(f"=== Silent-miss activation forensics (threshold {args.threshold}, "
+          f"{n_miss} misses in {len(by_pano)} panos, #46 Phase 1) ===", flush=True)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = ts.load_model().to(device)
+    print(f"device={device} model=projectsidewalk/rampnet-model "
+          f"(single-pass fp32, as op_cache)", flush=True)
+
+    rng = random.Random(NULL_SEED)
+    radius_sq = radius_sq_for()
+    results, skipped = [], 0
+    for i, ((city, pano), misses) in enumerate(sorted(by_pano.items()), 1):
+        path = pano_path(city, pano, args.panos_root)
+        if not os.path.exists(path):
+            skipped += len(misses)
+            continue
+        heat = ts.heatmap_for(model, device, path, use_fp16=False)
+        for r in misses:
+            act, pct, null_med, null_p95 = null_percentile(
+                heat, r["x"], r["y"], rng, radius_sq=radius_sq)
+            _, off_px, center = site_profile(heat, r["x"], r["y"], radius_sq)
+            npk_px, npk_score = nearest_peak(preds_by.get((city, pano), []),
+                                             r["x"], r["y"])
+            key = row_key(r)
+            v = rated_by_rowkey.get(key)
+            results.append({
+                "city": city, "pano": pano, "x": r["x"], "y": r["y"],
+                "field": r["field"], "dist_m": round(r["dist"], 1),
+                "px": round(r["px"], 1),
+                "group": group_of(r, queue_keys, rated_by_rowkey),
+                "verdict": v["verdict"] if v else None,
+                "act": round(act, 5), "null_pct": round(pct, 3),
+                "null_med": round(null_med, 5), "null_p95": round(null_p95, 5),
+                "above_own_null_p95": act > null_p95,
+                "argmax_off_px": round(off_px, 1),
+                "act_at_site": round(center, 5),
+                "nearest_peak_px": (round(npk_px, 1)
+                                    if npk_px != float("inf") else None),
+                "nearest_peak_score": (round(npk_score, 3)
+                                       if npk_score is not None else None),
+            })
+        del heat
+        if i % 10 == 0:
+            print(f"  {i}/{len(by_pano)} panos", flush=True)
+        if args.limit and i >= args.limit:
+            print(f"  --limit {args.limit} reached", flush=True)
+            break
+    if skipped:
+        print(f"  [!] {skipped} misses skipped — panorama not on disk under "
+              f"{args.panos_root}", flush=True)
+
+    # ----------------------------------------------------------------------- #
+    print(f"\n{'-'*78}\nACTIVATION AT THE MISSED RAMP, by field and stratum\n{'-'*78}")
+    print(f"{'population':>34} {'n':>4} {'act q1/med/q3':>18} "
+          f"{'>p95 of own null':>17} {'act>=0.01':>10}")
+    groups = {}
+    for field in ("near", "far"):
+        for grp in ("rated", "below_floor", "witnessed"):
+            sel = [r for r in results if r["field"] == field and r["group"] == grp]
+            if not sel:
+                continue
+            name = f"{field} / {grp}"
+            groups[name] = sel
+    for name, sel in list(groups.items()) + [("ALL silent misses", results)]:
+        q = quartiles([r["act"] for r in sel])
+        n_sig = sum(1 for r in sel if r["above_own_null_p95"])
+        n_01 = sum(1 for r in sel if r["act"] >= 0.01)
+        print(f"{name:>34} {len(sel):>4} "
+              f"{q[0]:>6.4f}/{q[1]:>6.4f}/{q[2]:>6.4f} "
+              f"{n_sig:>7}/{len(sel):<7} {n_01:>10}")
+
+    vis = [r for r in results if r["verdict"] == "visible"]
+    if vis:
+        q = quartiles([r["act"] for r in vis])
+        n_sig = sum(1 for r in vis if r["above_own_null_p95"])
+        print(f"\n  rated `visible` only (n={len(vis)}): act q1/med/q3 "
+              f"{q[0]:.4f}/{q[1]:.4f}/{q[2]:.4f}; {n_sig}/{len(vis)} above their "
+              f"own pano's null p95")
+
+    # What the in-window mass actually IS. A silent miss has no floor peak in
+    # radius by definition, so act >= 0.05 can only be an outside mode's tail;
+    # the argmax offset and the nearest cached peak make that checkable rather
+    # than asserted.
+    print(f"\n{'-'*78}\nDECOMPOSITION — what the in-window response is\n{'-'*78}")
+    r_px = radius_sq ** 0.5
+    cls = {"absent": [], "faint_local": [], "tail": []}
+    for r in results:
+        if r["act"] < 0.01:
+            cls["absent"].append(r)
+        elif r["act"] >= 0.05:
+            cls["tail"].append(r)
+        else:
+            cls["faint_local"].append(r)
+    for name, sel in cls.items():
+        if not sel:
+            continue
+        med_off = quartiles([r["argmax_off_px"] for r in sel])[1]
+        npks = [r["nearest_peak_px"] for r in sel if r["nearest_peak_px"]]
+        med_npk = quartiles(npks)[1] if npks else float("nan")
+        n_vis = sum(1 for r in sel if r["verdict"] == "visible")
+        print(f"  {name:>12}: {len(sel):>3}  (rated visible {n_vis:>2})  "
+              f"argmax off med {med_off:>4.1f} px  nearest floor peak med "
+              f"{med_npk:>5.1f} px ({med_npk/r_px:.1f}R)")
+    tail_near_edge = sum(1 for r in cls['tail']
+                         if r['argmax_off_px'] > 0.75 * r_px)
+    print(f"  tail cases with argmax in the window's outer quarter: "
+          f"{tail_near_edge}/{len(cls['tail'])} — the mass is entering from "
+          f"outside, not centred on the ramp")
+
+    print(f"\n  Reading: 'absent' = the heatmap is genuinely flat at the site.")
+    print(f"  'faint_local' = a real sub-floor response at the site itself.")
+    print(f"  'tail' = a neighbouring supra-floor mode's slope reaches the window —")
+    print(f"  the site contributed no mode of its own, but the model responded to")
+    print(f"  something adjacent (cf. the merged bucket's sigma story). The three")
+    print(f"  continue differently: absent -> Phase 2's scale counterfactual;")
+    print(f"  faint_local -> threshold/calibration (the sub_threshold continuum);")
+    print(f"  tail -> representation (sigma), not vocabulary.")
+
+    if args.json_out:
+        os.makedirs(os.path.dirname(os.path.abspath(args.json_out)), exist_ok=True)
+        with open(args.json_out, "w", encoding="utf-8") as fh:
+            json.dump({"threshold": args.threshold, "null_trials": NULL_TRIALS,
+                       "null_seed": NULL_SEED, "n": len(results),
+                       "skipped_no_imagery": skipped,
+                       "model": "projectsidewalk/rampnet-model",
+                       "tta": False, "results": results}, fh, indent=2)
+        print(f"\nWrote {args.json_out}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/tests/test_farfield_forensics.py b/tests/test_farfield_forensics.py
new file mode 100644
index 0000000..1d6b100
--- /dev/null
+++ b/tests/test_farfield_forensics.py
@@ -0,0 +1,197 @@
+"""Tests for the far-field anomaly's Phase 0 sample forensics (#46).
+
+Two layers, matching the script:
+
+* pure-function tests — the AUC, the per-split floor translation, the band
+  classifier (including the above-horizon clamp), and the matched-size rate,
+  since each one carries a headline claim;
+* integration tests against the **committed** inputs (`analysis_out/op_cache`,
+  `analysis_out/silent_witness.json`, `benchmark/miss_taxonomy_46/`,
+  `benchmark/<city>/imagery_manifest.json`) pinning the population arithmetic the
+  write-up quotes: 83 far-field silent misses = 37 rated + 9 below the floor +
+  37 witnessed. If a cache or manifest changes, these numbers must be re-derived,
+  not assumed — that is exactly the failure this file exists to catch.
+
+CPU-only, no network, no imagery, no GPU.
+"""
+import json
+import os
+import sys
+
+import pytest
+
+REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, REPO)
+sys.path.insert(0, os.path.join(REPO, "scripts", "analysis"))
+
+import farfield_forensics as ff  # noqa: E402
+
+
+# --------------------------------------------------------------------------- #
+# auc — the survivorship and discrimination statistic
+# --------------------------------------------------------------------------- #
+def test_auc_is_half_for_identical_samples():
+    assert ff.auc([1, 2, 3], [1, 2, 3]) == pytest.approx(0.5)
+
+
+def test_auc_is_one_when_a_dominates():
+    assert ff.auc([10, 11], [1, 2]) == 1.0
+
+
+def test_auc_is_zero_when_b_dominates():
+    assert ff.auc([1, 2], [10, 11]) == 0.0
+
+
+def test_auc_counts_ties_half():
+    # a = [1], b = [1]: one comparison, tied.
+    assert ff.auc([1], [1]) == pytest.approx(0.5)
+
+
+def test_auc_empty_is_nan():
+    assert ff.auc([], [1]) != ff.auc([], [1])  # NaN
+
+
+# --------------------------------------------------------------------------- #
+# effective_floor_model_px — the per-split floor translation
+# --------------------------------------------------------------------------- #
+def test_floor_on_a_16384_split_is_seven_and_a_half_model_px():
+    assert ff.effective_floor_model_px(16384) == pytest.approx(7.5)
+
+
+def test_floor_at_parity_is_the_floor_itself():
+    assert ff.effective_floor_model_px(4096) == pytest.approx(30.0)
+
+
+def test_floor_scales_inversely_with_stored_width():
+    assert ff.effective_floor_model_px(8000) == pytest.approx(30.0 * 4096 / 8000)
+
+
+# --------------------------------------------------------------------------- #
+# band_of — the far bands and the clamp
+# --------------------------------------------------------------------------- #
+def _row(dist, y=0.7):
+    return {"dist": dist, "y": y}
+
+
+def test_band_edges_are_half_open():
+    assert ff.band_of(_row(18.0)) == (18.0, 25.0)
+    assert ff.band_of(_row(25.0)) == (25.0, 40.0)
+    assert ff.band_of(_row(40.0)) == (40.0, 150.0)
+
+
+def test_above_horizon_is_the_clamp_band_regardless_of_distance():
+    # geom() sends y <= 0.5 to 150 m; the y is the tell, not the distance.
+    assert ff.band_of(_row(150.0, y=0.5)) == "clamp"
+    assert ff.band_of(_row(150.0, y=0.4)) == "clamp"
+
+
+def test_near_field_rows_fall_in_no_band():
+    assert ff.band_of(_row(10.0)) is None
+
+
+# --------------------------------------------------------------------------- #
+# matched_rate — "how often does the model find OTHER ramps this size?"
+# --------------------------------------------------------------------------- #
+def _sized(px, hit):
+    return {"px": px, "hit": hit}
+
+
+def test_matched_rate_counts_only_rows_within_tolerance():
+    rows = [_sized(20, True), _sized(24, False), _sized(50, True)]
+    hits, n = ff.matched_rate(rows, 20.0, tol=0.20)
+    assert (hits, n) == (1, 2)  # the 50-px row is out of band
+
+
+def test_matched_rate_tolerance_is_symmetric_and_inclusive():
+    rows = [_sized(16.0, True), _sized(24.0, False)]
+    hits, n = ff.matched_rate(rows, 20.0, tol=0.20)
+    assert (hits, n) == (1, 2)
+
+
+def test_matched_rate_with_no_neighbours_is_zero_of_zero():
+    assert ff.matched_rate([_sized(100, True)], 20.0) == (0, 0)
+
+
+# --------------------------------------------------------------------------- #
+# percentile_rank / quartiles
+# --------------------------------------------------------------------------- #
+def test_percentile_rank_midpoint():
+    assert ff.percentile_rank([1, 2, 3, 4], 2.5) == pytest.approx(0.5)
+
+
+def test_percentile_rank_ties_count_half():
+    assert ff.percentile_rank([1, 2, 2, 3], 2) == pytest.approx(0.5)
+
+
+def test_quartiles_are_ordered():
+    q1, med, q3 = ff.quartiles(list(range(100)))
+    assert q1 < med < q3
+
+
+# --------------------------------------------------------------------------- #
+# Integration — the committed populations the write-up quotes
+# --------------------------------------------------------------------------- #
+WITNESS = os.path.join(REPO, "analysis_out", "silent_witness.json")
+GALLERY = os.path.join(REPO, "benchmark", "miss_taxonomy_46")
+
+needs_committed = pytest.mark.skipif(
+    not (os.path.exists(WITNESS)
+         and os.path.exists(os.path.join(GALLERY, "silent__jonf.json"))),
+    reason="committed witness/gallery files not present")
+
+
+@pytest.fixture(scope="module")
+def far_populations():
+    import miss_taxonomy as mt
+    from miss_decomposition import DEFAULT_THRESHOLD, US_SPLITS
+    pooled = []
+    for city in US_SPLITS:
+        loaded = mt.load_rows(city, DEFAULT_THRESHOLD, rng=None)
+        if loaded is not None:
+            pooled.extend(loaded[0])
+    far_silent = [r for r in pooled if r["field"] == "far" and not r["hit"]
+                  and r["bucket"] == "silent"]
+    from miss_gallery import load_queue
+    queue = load_queue(WITNESS)
+    unw = [r for r in far_silent if ff.row_key(r) in queue]
+    rated = ff.load_rated(GALLERY)
+    rated_keys = {(v["city"], v["pano"], round(float(v["x"]), 6),
+                   round(float(v["y"]), 6)) for v in rated.values()}
+    return far_silent, unw, rated, rated_keys
+
+
+@needs_committed
+def test_the_population_arithmetic_the_writeup_quotes(far_populations):
+    far_silent, unw, rated, rated_keys = far_populations
+    assert len(far_silent) == 83
+    assert len(unw) == 46
+    assert len(rated) == 37
+    below = [r for r in unw if ff.row_key(r) not in rated_keys]
+    assert len(below) == 9
+
+
+@needs_committed
+def test_every_rated_item_is_an_unwitnessed_far_silent_miss(far_populations):
+    far_silent, unw, rated, rated_keys = far_populations
+    unw_keys = {ff.row_key(r) for r in unw}
+    assert rated_keys <= unw_keys
+
+
+@needs_committed
+def test_the_far_verdict_tally_matches_the_committed_pass(far_populations):
+    _, _, rated, _ = far_populations
+    tally = {}
+    for v in rated.values():
+        tally[v["verdict"]] = tally.get(v["verdict"], 0) + 1
+    assert tally == {"visible": 34, "context-only": 2, "unclear": 1}
+
+
+@needs_committed
+def test_imagery_manifests_cover_every_rated_pano():
+    # The floor table translates the source-px floor through the committed widths;
+    # a rated pano missing from its manifest would silently drop from that table.
+    rated = ff.load_rated(GALLERY)
+    for v in rated.values():
+        widths = ff.stored_widths(v["city"])
+        assert v["pano"] in widths, (v["city"], v["pano"])
+        assert widths[v["pano"]] == v["source_width"]
diff --git a/tests/test_silent_activation.py b/tests/test_silent_activation.py
new file mode 100644
index 0000000..3de2bb6
--- /dev/null
+++ b/tests/test_silent_activation.py
@@ -0,0 +1,147 @@
+"""Unit tests for the silent-miss activation forensics (#46, Phase 1).
+
+Pure core only — no torch, no imagery, no GPU. The heavy path (model inference)
+is exercised by running the script itself; what these protect is the geometry:
+``radius_max`` must read the heatmap through exactly the matcher's coordinate
+convention, or the activation numbers describe the wrong locations.
+"""
+import os
+import random
+import sys
+
+import pytest
+
+REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, REPO)
+sys.path.insert(0, os.path.join(REPO, "scripts", "analysis"))
+
+import silent_activation as sa  # noqa: E402
+from rampnet.detection_eval import radius_sq_for  # noqa: E402
+
+RSQ = radius_sq_for()
+R = RSQ ** 0.5  # 22.5 heatmap px
+
+
+def _heat(value=0.0):
+    return [[value] * 1024 for _ in range(512)]
+
+
+# --------------------------------------------------------------------------- #
+# radius_max — the matcher's window, applied to the heatmap
+# --------------------------------------------------------------------------- #
+def test_reads_a_peak_at_the_site():
+    h = _heat()
+    h[256][512] = 0.7
+    assert sa.radius_max(h, 512 / 1024, 256 / 512) == pytest.approx(0.7)
+
+
+def test_ignores_a_peak_outside_the_radius():
+    h = _heat()
+    h[256][512 + int(R) + 2] = 0.9
+    assert sa.radius_max(h, 512 / 1024, 256 / 512) == 0.0
+
+
+def test_sees_a_peak_just_inside_the_radius():
+    h = _heat()
+    h[256][512 + int(R) - 1] = 0.9
+    assert sa.radius_max(h, 512 / 1024, 256 / 512) == pytest.approx(0.9)
+
+
+def test_columns_wrap_at_the_seam():
+    # A site at x~0 must see a peak stored at the right edge of the heatmap.
+    h = _heat()
+    h[256][1023] = 0.8
+    assert sa.radius_max(h, 2 / 1024, 256 / 512) == pytest.approx(0.8)
+
+
+def test_rows_clamp_at_the_top():
+    # A site near the top row must not crash reaching above the panorama.
+    h = _heat()
+    h[0][512] = 0.6
+    assert sa.radius_max(h, 512 / 1024, 0.0) == pytest.approx(0.6)
+
+
+def test_values_clip_to_one_like_peak_extraction():
+    h = _heat()
+    h[256][512] = 1.7
+    assert sa.radius_max(h, 512 / 1024, 256 / 512) == 1.0
+
+
+# --------------------------------------------------------------------------- #
+# null_percentile — signal against the pano's own noise floor
+# --------------------------------------------------------------------------- #
+def test_flat_heatmap_reads_as_chance():
+    act, pct, med, p95 = sa.null_percentile(_heat(0.003), 0.5, 0.5,
+                                            random.Random(0), trials=50)
+    assert act == pytest.approx(0.003)
+    assert pct == pytest.approx(0.5)  # every draw ties the site
+    assert med == p95 == pytest.approx(0.003)
+
+
+def test_a_lone_bump_at_the_site_beats_its_null():
+    h = _heat()
+    h[256][512] = 0.04
+    act, pct, _, p95 = sa.null_percentile(h, 512 / 1024, 256 / 512,
+                                          random.Random(0), trials=100)
+    assert act == pytest.approx(0.04)
+    assert pct > 0.9
+    assert act > p95
+
+
+def test_a_site_no_better_than_the_horizon_band_fails_the_test():
+    # Strong response everywhere along the site's row: the site is nothing special.
+    h = _heat()
+    for c in range(0, 1024, 8):
+        h[256][c] = 0.5
+    act, pct, _, p95 = sa.null_percentile(h, 512 / 1024, 256 / 512,
+                                          random.Random(0), trials=100)
+    assert act == pytest.approx(0.5)
+    assert not act > p95
+
+
+# --------------------------------------------------------------------------- #
+# site_profile / nearest_peak — separating a site response from a neighbour's tail
+# --------------------------------------------------------------------------- #
+def test_site_profile_centred_bump_has_zero_offset():
+    h = _heat()
+    h[256][512] = 0.4
+    act, off, center = sa.site_profile(h, 512 / 1024, 256 / 512)
+    assert act == pytest.approx(0.4)
+    assert off == pytest.approx(0.0)
+    assert center == pytest.approx(0.4)
+
+
+def test_site_profile_offset_bump_reports_its_distance():
+    h = _heat()
+    h[256][512 + 15] = 0.4
+    act, off, center = sa.site_profile(h, 512 / 1024, 256 / 512)
+    assert act == pytest.approx(0.4)
+    assert off == pytest.approx(15.0)
+    assert center == 0.0  # nothing at the ramp itself
+
+
+def test_nearest_peak_measures_in_matcher_units_and_wraps():
+    # A peak across the seam: x=0.999 vs site x=0.001 is ~2 px away, not ~1022.
+    d, score = sa.nearest_peak([(0.999, 0.5, 0.7)], 0.001, 0.5)
+    assert d == pytest.approx(0.002 * 1024, abs=0.01)
+    assert score == 0.7
+
+
+def test_nearest_peak_with_no_peaks_is_infinite():
+    d, score = sa.nearest_peak([], 0.5, 0.5)
+    assert d == float("inf") and score is None
+
+
+# --------------------------------------------------------------------------- #
+# group_of — Phase 0's partition, reused
+# --------------------------------------------------------------------------- #
+def _row(city="bend", pano="p1", x=0.25, y=0.6):
+    return {"city": city, "pano": pano, "x": x, "y": y}
+
+
+def test_group_partition():
+    r = _row()
+    key = sa.row_key(r)
+    assert sa.group_of(r, set(), {}) == "witnessed"
+    assert sa.group_of(r, {key}, {}) == "below_floor"
+    assert sa.group_of(r, {key}, {key: {}}) == "rated"