{"prompt":"Match each driving condition to its primary-strength sensor and the weak-link sensor that fails there","map":[{"condition":"Pitch-dark rural road","primaryStrength":"Lidar","weakLink":"Camera","correct":true},{"condition":"Dense fog on the motorway","primaryStrength":"Radar","weakLink":"Camera","correct":true},{"condition":"Child behind a parked van","primaryStrength":"Lidar","weakLink":"Occlusion","correct":true},{"condition":"Clear sunny highway","primaryStrength":"Camera","weakLink":"Radar","correct":true}],"passed":true,"attempt":1}

{"checkpointId":"example_iou_demo","code":"# WORKED EXAMPLE: Intersection-over-Union for one clean box pair.\n# Boxes are [x1, y1, x2, y2] in pixels. (Next slide: you do this on a tougher pair.)\n\ngt   = [60, 50, 200, 150]   # ground-truth box: where the object really is\npred = [68, 56, 206, 156]   # the detector's predicted box (a near-perfect fit)\n\ndef iou(a, b):\n    # 1) the SHARED rectangle (intersection): the overlap of the two boxes\n    ix1, iy1 = max(a[0], b[0]), max(a[1], b[1])\n    ix2, iy2 = min(a[2], b[2]), min(a[3], b[3])\n    inter = max(0, ix2 - ix1) * max(0, iy2 - iy1)\n\n    # 2) the COMBINED area (union): both boxes together, counted once\n    area_a = (a[2] - a[0]) * (a[3] - a[1])\n    area_b = (b[2] - b[0]) * (b[3] - b[1])\n    union = area_a + area_b - inter\n\n    return inter / union, inter, union\n\nscore, inter, union = iou(gt, pred)\nTHRESHOLD = 0.5\nverdict = \"HIT  (counts as a detection)\" if score >= THRESHOLD else \"MISS (no match)\"\n\nprint(f\"shared area    = {inter:6.0f} px\")\nprint(f\"combined area  = {union:6.0f} px\")\nprint(f\"IoU            = {score:6.3f}   (shared / combined)\")\nprint(f\"threshold      = {THRESHOLD}\")\nprint(f\"VERDICT        = {verdict}\")","stdout":"shared area    =  12408 px\ncombined area  =  15392 px\nIoU            =  0.806   (shared / combined)\nthreshold      = 0.5\nVERDICT        = HIT  (counts as a detection)\n","stderr":"","error":null,"imageCount":0,"durationMs":1,"ranAt":"2026-07-21T01:44:53.321Z"}

{"checkpointId":"ttcs_iou_classify_run","code":"# Run IoU on this pair, read the number, then classify it (Section 1).\n# Boxes are [x1, y1, x2, y2] in pixels. The object is REAL, there is a ground-truth box.\n\ngt   = [60, 50, 200, 150]   # ground truth: a real object is here\npred = [150, 96, 280, 196]   # the detector's box for this frame\n\ndef iou(a, b):\n    ix1, iy1 = max(a[0], b[0]), max(a[1], b[1])\n    ix2, iy2 = min(a[2], b[2]), min(a[3], b[3])\n    inter = max(0, ix2 - ix1) * max(0, iy2 - iy1)\n    union = (a[2]-a[0])*(a[3]-a[1]) + (b[2]-b[0])*(b[3]-b[1]) - inter\n    return inter / union\n\nscore = iou(gt, pred)\nprint(f\"IoU = {score:.3f}\")\nprint(f\"threshold = 0.5  ->  {'matches' if score >= 0.5 else 'does NOT match'} a ground-truth box\")\nprint(\"So: a real object, no matching box. Your call on the next panel.\")","stdout":"IoU = 0.111\nthreshold = 0.5  ->  does NOT match a ground-truth box\nSo: a real object, no matching box. Your call on the next panel.\n","stderr":"","error":null,"imageCount":0,"durationMs":1,"ranAt":"2026-07-21T01:46:55.989Z"}

{"prompt":"Confirm the IoU threshold at 0.5, compute IoU for the borderline pair, and classify it as hit / miss / phantom; define a false negative as an unmatched real object","iouThreshold":0.5,"pairIoU":0.111,"classification":"miss","falseNegativeDefinition":"a real (ground-truth) object with no predicted box matched at IoU >= 0.5","cellRun":true,"passed":true,"attempt":1}

{"checkpointId":"example_metrics_demo","code":"# WORKED EXAMPLE: score a tiny 8-row detections-vs-ground-truth split.\n# (Next slide: you do this on the detector's full table.)\nimport pandas as pd\n\n# Each row = one scored object. outcome is:\n#   \"TP\" a real object the detector caught,\n#   \"FN\" a real object it MISSED (a false negative),\n#   \"FP\" a phantom box over empty road (a false positive).\ndemo = pd.DataFrame([\n    (\"Pedestrian\", \"TP\"),   # 3 real pedestrians, 2 caught, 1 missed\n    (\"Pedestrian\", \"TP\"),\n    (\"Pedestrian\", \"FN\"),\n    (\"Cyclist\",    \"TP\"),   # 2 real cyclists, 1 caught, 1 missed\n    (\"Cyclist\",    \"FN\"),\n    (\"Car\",        \"TP\"),   # 2 real cars, both caught\n    (\"Car\",        \"TP\"),\n    (\"Car\",        \"FP\"),   # 1 phantom car (braked for nothing)\n], columns=[\"obj_class\", \"outcome\"])\n\n# 1) Confusion counts over the whole split.\nTP = (demo.outcome == \"TP\").sum()\nFP = (demo.outcome == \"FP\").sum()\nFN = (demo.outcome == \"FN\").sum()\nprint(f\"TP = {TP}   FP = {FP}   FN = {FN}\")\n\n# 2) Precision = of everything flagged, how much was real.\nprecision = TP / (TP + FP)\n# 3) Recall = of everything really there, how much we caught.\nrecall = TP / (TP + FN)\nprint(f\"precision = TP/(TP+FP) = {TP}/{TP+FP} = {precision:.2f}\")\nprint(f\"recall    = TP/(TP+FN) = {TP}/{TP+FN} = {recall:.2f}\")\n\n# 4) The safety number: recall on vulnerable road users (pedestrians + cyclists),\n#    then the false-negative RATE = the share of real VRUs we MISSED.\nvru = demo[demo.obj_class.isin([\"Pedestrian\", \"Cyclist\"])]\nvru_TP = (vru.outcome == \"TP\").sum()\nvru_FN = (vru.outcome == \"FN\").sum()\nvru_recall  = vru_TP / (vru_TP + vru_FN)\nvru_fn_rate = 1 - vru_recall\nprint(f\"VRU recall  = {vru_TP}/{vru_TP+vru_FN} = {vru_recall:.2f}\")\nprint(f\"VRU FN-rate = 1 - VRU recall = {vru_fn_rate:.2f}   <- the safety number\")","stdout":"TP = 5   FP = 1   FN = 2\nprecision = TP/(TP+FP) = 5/6 = 0.83\nrecall    = TP/(TP+FN) = 5/7 = 0.71\nVRU recall  = 3/5 = 0.60\nVRU FN-rate = 1 - VRU recall = 0.40   <- the safety number\n","stderr":"","error":null,"imageCount":0,"durationMs":5,"ranAt":"2026-07-21T01:50:48.959Z"}

{"checkpointId":"ttcs_scored_metrics","code":"# Section 2: score the Aurora-7 detector on its detections table.\n# SYNTHETIC, illustrative detections-vs-ground-truth (no raw imagery, no model runs here;\n# proportions grounded in the published KITTI/nuScenes benchmarks).\n# Each row = one scored object, tagged with class + condition, with an outcome:\n#   \"TP\" caught,  \"FN\" a real object MISSED (false negative),  \"FP\" a phantom box.\nimport pandas as pd\n\n# Read the SAME scored detections table Act 3 slices by condition, so Section 2 and\n# Section 3 can never disagree. (Normalise two column names so the scoring below reads\n# cleanly: class -> obj_class, flag -> outcome.)\ndf = pd.read_csv(\"aurora7_detections_kitti.csv\").rename(\n    columns={\"class\": \"obj_class\", \"flag\": \"outcome\"})\nprint(f\"scored objects in table: {len(df)}\")\n\ndef score(d, label):\n    TP = (d.outcome == \"TP\").sum()\n    FP = (d.outcome == \"FP\").sum()\n    FN = (d.outcome == \"FN\").sum()\n    precision = TP / (TP + FP)\n    recall    = TP / (TP + FN)\n    print(f\"{label:18s} TP={TP:4d} FP={FP:3d} FN={FN:3d}  \"\n          f\"precision={precision:.2f}  recall={recall:.2f}\")\n    return recall\n\n# 1) The whole table, the headline (aggregate) numbers.\nagg_recall = score(df, \"ALL (aggregate)\")\n\n# 2) Vulnerable road users only = pedestrians + cyclists. THIS is the safety subset.\nvru = df[df.obj_class.isin([\"Pedestrian\", \"Cyclist\"])]\nvru_recall = score(vru, \"VRU (Ped+Cyc)\")\n\n# 3) The safety number: VRU false-negative RATE = share of real VRUs MISSED.\nvru_fn_rate = 1 - vru_recall\nprint(\"-\" * 60)\nprint(f\"AGGREGATE recall (all classes) : {agg_recall:.2f}   <- looks reassuring\")\nprint(f\"VRU false-negative rate        : {vru_fn_rate:.2f}   <- the safety number\")","stdout":"scored objects in table: 991\nALL (aggregate)    TP= 822 FP= 41 FN=128  precision=0.95  recall=0.87\nVRU (Ped+Cyc)      TP= 392 FP= 35 FN=122  precision=0.92  recall=0.76\n------------------------------------------------------------\nAGGREGATE recall (all classes) : 0.87   <- looks reassuring\nVRU false-negative rate        : 0.24   <- the safety number\n","stderr":"","error":null,"imageCount":0,"durationMs":10,"ranAt":"2026-07-21T01:51:08.790Z"}

{"prompt":"Score the detector: read off precision, aggregate recall, and the VRU false-negative rate (the safety number)","aggregatePrecision":0.95,"aggregateRecall":0.87,"vruRecall":0.76,"vruFalseNegativeRate":0.236,"safetyNumber":"VRU false-negative rate","readOffBand":"20–30%","readOffCorrect":true,"scoringCellRan":true,"passed":true,"attempt":1}

{"checkpointId":"ttcs_example_slice","code":"# Worked example: slice VRU recall by ONE condition (day vs night).\n# Same move you'll run for every condition next, just for one cut here.\nimport pandas as pd\n\ndf = pd.read_csv(\"aurora7_detections_kitti.csv\")\n\n# Vulnerable road users = pedestrians + cyclists. Keep REAL objects only\n# (a TP was caught, an FN was a real person missed; phantoms are false positives).\nvru = df[df[\"class\"].isin([\"Pedestrian\", \"Cyclist\"])]\nreal = vru[vru[\"flag\"].isin([\"TP\", \"FN\"])]\n\ndef recall(rows):\n    tp = (rows[\"flag\"] == \"TP\").sum()\n    fn = (rows[\"flag\"] == \"FN\").sum()\n    return tp / (tp + fn)            # share of real VRUs the detector actually caught\n\nfor light in [\"day\", \"night\"]:\n    slice_ = real[real[\"lighting\"] == light]\n    r = recall(slice_)\n    print(f\"{light:>5}:  recall = {r:.2f}   ->  {(1-r)*100:4.0f}% of real VRUs missed\")\n\nprint()\nprint(f\"Both together:  recall = {recall(real):.2f}   (the reassuring average)\")","stdout":"  day:  recall = 0.85   ->    15% of real VRUs missed\nnight:  recall = 0.39   ->    61% of real VRUs missed\n\nBoth together:  recall = 0.76   (the reassuring average)\n","stderr":"","error":null,"imageCount":0,"durationMs":10,"ranAt":"2026-07-21T01:55:33.833Z"}

{"prompt":"Slice VRU recall per condition, then triage the failure modes by (condition likelihood x miss impact); do not bury a rare-but-catastrophic VRU miss in green","aggregateVruRecall":0.76,"perConditionRecall":[{"condition":"Daylight","recall":0.85},{"condition":"Clear","recall":0.83},{"condition":"Common class","recall":0.79},{"condition":"Rare class (cyclist)","recall":0.66},{"condition":"Occluded","recall":0.45},{"condition":"Night","recall":0.39},{"condition":"Night + occluded","recall":0.27}],"worstCondition":"Night + occluded","worstConditionRecall":0.27,"triage":[{"failureMode":"Night-time pedestrian miss","recallSlice":"night 0.39","likelihood":"Common","impact":"Catastrophic","severityBand":"red"},{"failureMode":"Occluded child at night","recallSlice":"night+occluded 0.27","likelihood":"Occasional","impact":"Catastrophic","severityBand":"red"},{"failureMode":"Rare-class cyclist miss","recallSlice":"rare 0.66","likelihood":"Rare","impact":"Catastrophic","severityBand":"red"},{"failureMode":"Occluded pedestrian (daytime)","recallSlice":"occluded 0.45","likelihood":"Occasional","impact":"Serious","severityBand":"amber"},{"failureMode":"Night glare phantom brake","recallSlice":"precision dip","likelihood":"Common","impact":"Minor","severityBand":"amber"},{"failureMode":"Clear-daylight car miss","recallSlice":"day 0.99","likelihood":"Rare","impact":"Minor","severityBand":"green"},{"failureMode":"Dusk / low-sun pedestrian","recallSlice":"transition","likelihood":"Occasional","impact":"Catastrophic","severityBand":"red"}],"topBandFailureModes":["Night-time pedestrian miss","Occluded child at night","Rare-class cyclist miss","Occluded pedestrian (daytime)","Night glare phantom brake","Dusk / low-sun pedestrian"],"passed":true,"attempt":2}

{"checkpointId":"ttcs_triage_slice","code":"# Section 3: slice VRU recall over EVERY condition in the table.\nimport pandas as pd\n\ndf = pd.read_csv(\"aurora7_detections_kitti.csv\")\nreal = df[(df[\"class\"].isin([\"Pedestrian\", \"Cyclist\"])) & (df[\"flag\"].isin([\"TP\", \"FN\"]))]\n\ndef recall(rows):\n    tp = (rows[\"flag\"] == \"TP\").sum(); fn = (rows[\"flag\"] == \"FN\").sum()\n    return tp / (tp + fn) if (tp + fn) else float(\"nan\")\n\nprint(f\"All conditions (the average):  {recall(real):.2f}\\n\")\nprint(\"By condition  ->  recall   (% of real VRUs missed)\")\nfor col, vals in [(\"lighting\", [\"day\", \"night\"]),\n                  (\"visibility\", [\"clear\", \"occluded\"]),\n                  (\"rarity\", [\"common\", \"rare\"])]:\n    for v in vals:\n        s = real[real[col] == v]\n        r = recall(s)\n        print(f\"  {v:<10} {r:.2f}     {(1-r)*100:4.0f}% missed\")\n\nworst = real[(real[\"lighting\"] == \"night\") & (real[\"visibility\"] == \"occluded\")]\nprint(f\"\\nWorst slice (night + occluded): {recall(worst):.2f}   \"\n      f\"{(1-recall(worst))*100:.0f}% of those people missed\")","stdout":"All conditions (the average):  0.76\n\nBy condition  ->  recall   (% of real VRUs missed)\n  day        0.85       15% missed\n  night      0.39       61% missed\n  clear      0.83       17% missed\n  occluded   0.45       55% missed\n  common     0.79       21% missed\n  rare       0.66       34% missed\n\nWorst slice (night + occluded): 0.27   73% of those people missed\n","stderr":"","error":null,"imageCount":0,"durationMs":25,"ranAt":"2026-07-21T02:20:41.814Z"}

{"prompt":"For each top failure mode write one ISO 21448 SOTIF chain (triggering condition -> functional insufficiency -> hazardous behaviour -> concrete mitigation); a vague mitigation or a SOTIF gap framed as a fault fails","rows":[{"failureMode":"Occluded child at night","triggeringCondition":"zero ambient light (nighttime darkness) combined with total physical line of sight occlusion","functionalInsufficiency":"The camera fails due to darkness, and the LiDAR/Radar are physically blocked by the van. The system works as designed but cannot infer the hidden pedestrian, resulting in a catastrophic perception gap with a measured recall of just 0.27.","hazardousBehaviour":"The perception system fails to pass a target to the control system, resulting in an absent or critically late Automatic Emergency Braking command and a direct collision with the pedestrian.","mitigation":"restriction limiting the system to a daylight only cap. Alternatively, enforce a mandatory driver attention rule requiring the human driver to take over in urban/residential environments at night.","mitigationIsConcrete":true,"framedAsFault":false},{"failureMode":"Rare-class cyclist miss","triggeringCondition":"the detector wasn’t exposed to enough cyclist examples, so it may recognize cyclists less reliably than common objects like cars.","functionalInsufficiency":"The system hardware works exactly as designed, but the software's training distribution lacks sufficient cyclist examples.","hazardousBehaviour":"The perception system fails to classify the cyclist as an obstacle, meaning no target is passed to the control system. The vehicle fails to engage the Automatic Emergency Braking.","mitigation":"Enforce a targeted data collection and retraining cyclist data before deployment, and implement a mandatory sensor-fusion fallback relying on Radar (which detects moving metallic objects like bikes regardless of visual classification).","mitigationIsConcrete":true,"framedAsFault":false}],"mitigations":["restriction limiting the system to a daylight only cap. Alternatively, enforce a mandatory driver attention rule requiring the human driver to take over in urban/residential environments at night.","Enforce a targeted data collection and retraining cyclist data before deployment, and implement a mandatory sensor-fusion fallback relying on Radar (which detects moving metallic objects like bikes regardless of visual classification)."],"passed":true,"attempt":1}

{"prompt":"Write the GO / GO-WITH-CONDITIONS / NO-GO ship verdict, each of five fields citing its own dossier section number, internally consistent (no unconditional GO over an unmitigated night miss; no NO-GO if a daylight-restricted release is viable)","verdict":"CONDITIONS","fields":{"definition":"A valid detection is registered when the predicted bounding box overlaps the ground truth with an IoU ≥ 0.5. Anything below this geometric threshold is classified as a missed object (a false negative).","metrics":"While the aggregate numbers look fine, the model struggles with vulnerable road users. The VRU recall is 0.76, which leaves us with a catastrophic VRU false-negative rate of 24%.","worst":"The system completely collapses in compound edge cases. In the worst-case scenario—an occluded child at night—the sensor stack is blinded, and the worst slice drops to 0.27 recall.","mitigation":"Because the system cannot currently be trusted to identify humans in the dark, we must implement a strict restriction limiting the system to a daylight-only cap.","ask":"I am asking the board to approve a GO WITH CONDITIONS. The Aurora-7 detector is approved for deployment strictly under a daylight-only cap, and cannot be authorized for nighttime operation until the radar-fusion fallback is thoroughly retrained and re-validated."},"citedSections":["Detection Definition","Metrics","Worst Failure Mode","Mitigation","The Ask"],"internallyConsistent":true,"passed":true,"attempt":1}

{"guess":"Yes, a sensor on the outside can hear it","index":0}

{"checkpointId":"schaeffler_load_plot","code":"# STEP 1 of your detector: plot the FAULTY bearing so you can SEE the fault.\nimport numpy as np, matplotlib.pyplot as plt\n\nfs = 20_000                          # sampling rate: 20,000 readings/second\nt  = np.arange(0, 0.05, 1/fs)        # a 50-ms window\nrng = np.random.default_rng(7)\n\n# ▼▼▼ CHANGE THIS ONE LINE: load the faulty snapshot, not the healthy one ▼▼▼\nSNAPSHOT = \"faulty\"     # <-- set this to \"faulty\"\n# ▲▲▲ then press \"Run cell\" and watch the plot change ▲▲▲\n\n# (Both snapshots come from the same housing-resonance + noise. The FAULTY one\n#  also carries a sharp KNOCK every time a ball rolls over the spall — about\n#  once every 4 ms here. This is realistic physics-based SYNTHETIC vibration,\n#  modeled on documented bearing-fault signatures — not real measured data.)\nbase = 0.6*np.sin(2*np.pi*3000*t)*np.exp(-40*(t % 0.004)) + rng.normal(0, 0.5, t.size)\nif SNAPSHOT == \"faulty\":\n    knock = np.zeros_like(t)\n    for strike in np.arange(0.0, 0.05, 0.004):       # an impulse every 4 ms\n        k = np.abs(t - strike) < 1/fs\n        knock += 6.0 * k                              # the sharp impact\n    signal = base + knock\nelse:\n    signal = base\n\nplt.figure(figsize=(7, 2.6))\nplt.plot(t*1000, signal, lw=0.6,\n         color=\"#B45309\" if SNAPSHOT == \"faulty\" else \"#0E9F6E\")\nplt.title(f\"{SNAPSHOT.capitalize()} bearing — vibration over 50 ms\")\nplt.xlabel(\"time (ms)\"); plt.ylabel(\"acceleration (g)\")\nplt.tight_layout(); plt.show()\nprint(\"Plotted:\", SNAPSHOT, \"bearing —\", signal.size, \"samples\")","stdout":"Plotted: faulty bearing — 1000 samples\n","stderr":"","error":null,"imageCount":1,"durationMs":512,"ranAt":"2026-07-08T17:53:52.515Z"}

{"prompt":"After plotting the faulty bearing, identify what its waveform shows that the healthy one lacked.","selectedRead":"periodic","correctRead":"periodic","readCorrect":true,"passed":true,"attempt":1}

{"code":"import numpy as np\n\n# A healthy bearing snapshot: low, even shaking, no sharp knocks.\n# (In the lab you'll load real curated snapshots from CSV instead.)\nrng = np.random.default_rng(7)\nhealthy = rng.normal(0.0, 0.18, size=20000)   # 1 second @ 20 kHz\n\n# RMS, step by step:\nsquared = healthy ** 2          # 1) square every reading\nmean_sq = squared.mean()        # 2) average them\nrms     = np.sqrt(mean_sq)      # 3) square-root -> typical energy\n\nprint(f\"samples      : {healthy.size}\")\nprint(f\"RMS (healthy): {rms:.3f}\")","stdout":"samples      : 20000\nRMS (healthy): 0.179\n","stderr":"","error":null,"imageCount":0,"durationMs":2,"ranAt":"2026-07-08T17:56:48.976Z"}

{"code":"import numpy as np\n\n# Two curated 1-second snapshots (20 kHz). One healthy, one with a bearing fault.\n# (Generated inline here so the cell runs immediately; same shape as the real CSVs.)\nrng = np.random.default_rng(0)\ndef make(faulty):\n    base = rng.normal(0.0, 0.18, size=20000)         # healthy fuzz\n    if not faulty:\n        return base\n    knocks = np.zeros(20000)\n    knocks[::2500] = 1.0                              # a sharp knock every ~8th of a second\n    return base + np.convolve(knocks, np.hanning(40), mode='same') * 2.2\n\nhealthy = make(faulty=False)\nfaulty  = make(faulty=True)\n\ndef features(sig):\n    # 1) RMS — given. Square every reading, average, square-root.\n    rms = np.sqrt(np.mean(sig ** 2))\n\n    # 2) KURTOSIS — how spiky the signal is. Assemble it from these steps:\n    #    a) center the signal:        c = sig - sig.mean()\n    c = sig - sig.mean()\n    c4 = c ** 4\n    #    b) raise to the 4th power:    c4 = c ** 4\n    #    c) divide the mean of c4 by the standard deviation to the 4th power:\n    #       kurtosis = c4.mean() / (sig.std() ** 4)\n    kurtosis = c4.mean() / (sig.std() ** 4)    # <-- replace np.nan using steps (a)-(c) above\n\n    # 3) CREST FACTOR — how tall the biggest spike is next to the typical level.\n    #    crest = the largest absolute reading, divided by rms:\n    #       crest = np.max(np.abs(sig)) / rms\n    crest = np.max(np.abs(sig)) / rms      # <-- replace np.nan using the line above\n\n    return rms, kurtosis, crest\n\nfor name, sig in [(\"healthy\", healthy), (\"faulty\", faulty)]:\n    r, k, c = features(sig)\n    print(f\"{name:>7}:  rms={r:.3f}   kurtosis={k:.2f}   crest={c:.2f}\")","stdout":"healthy:  rms=0.179   kurtosis=3.00   crest=4.04\n faulty:  rms=0.244   kurtosis=28.57   crest=10.70\n","stderr":"","error":null,"imageCount":0,"durationMs":6,"ranAt":"2026-07-08T18:13:30.501Z"}

{"prompt":"Which defect frequency dominates the envelope spectrum (which names the cracked ring)?","choice":"BPFO","choiceRing":"Outer race","correctAnswer":"BPFO","correctRing":"Outer race","priorGuessRing":null,"passed":true,"attempt":1}

{"prompt":"Fit a depth-capped DecisionTree on TRAINING bearings only, then name the root-split feature and state the tree depth + node count against the embedded budget (depth ≤ 3, a few KB).","rootFeature":"kurtosis","rootCorrect":true,"statedDepth":1,"statedNodes":3,"withinBudget":true,"budgetDepthCap":3,"trainedOnTrainingOnly":true,"passed":true,"attempt":1}

{"prompt":"Run the detector on the held-out set (healthy + faulty, split by physical bearing), then read back catch-rate and false-alarm rate from the printout.","catchRate":0.83,"falseAlarmRate":0.25,"keyCatchRate":0.83,"keyFalseAlarmRate":0.25,"tolerance":0.1,"passed":true,"attempt":1}

{"prompt":"Set the alarm threshold on the held-out scores. Lower catches more faults but raises false alarms; pick the threshold you can defend on a cost basis.","threshold":0.41,"caught":12,"missed":0,"falseAlarms":3,"catchRatePct":100,"falseAlarmRatePct":25,"totalCost":1500,"costMissEur":20000,"costFalseAlarmEur":500,"locked":true}

{"prompt":"Write the one-page maintenance decision memo: chosen threshold, catch-rate, false-alarm rate, cost reasoning, which ring is cracked and how you know, plus one plain-language sentence for a non-expert manager. Use your own numbers.","memo":"the threshold you should lock is 0.41, where catch rate is 100% and has a false alarm of 25%, it has the lowest cost of 1500 Euros, The rate caught 12/12 healthy readings. \nWe found the cracked ring because the envelope spectrum showed the largest peak at defect frequency X, which corresponds to the outer ring on this bearing at 2000 RPM.\npaying €500 for a technician to quickly check a healthy machine is a minor operational cost compared to missing a fault and paying €20,000 when the entire production line crashes.","checklistCovered":["threshold","catch","falseAlarm","cost","fault","plain"],"passed":true,"attempt":1}

{"code":"import numpy as np\n\n# envelope_spectrum(signal, fs) is PROVIDED (plain numpy). It returns\n# (freqs, amp): the frequency axis and the envelope-spectrum amplitude.\nfreqs, amp = envelope_spectrum(signal, fs=20000)\n\n# The four SUPPLIED defect frequencies (Rexnord ZA-2115 @ 2000 RPM):\ndefect_hz = {\"FTF\": 14.7, \"BSF\": 70.9, \"BPFO\": 236.4, \"BPFI\": 296.9}\n\ndef band_energy(freqs, amp, centre, half_width=3.0):\n    # Build a boolean mask for the frequency band\n    mask = np.abs(freqs - centre) <= half_width\n    \n    # Sum the amplitude over that mask and return as a float\n    return float(np.sum(amp[mask]))\n\nenergy = {name: band_energy(freqs, amp, hz) for name, hz in defect_hz.items()}\ndominant = max(energy, key=energy.get)\n\nfor name, e in energy.items():\n    print(f\"{name:5s}  {e:8.2f}\")\nprint(\"dominant:\", dominant)","stdout":"FTF        0.01\nBSF        0.01\nBPFO       0.50\nBPFI       0.01\ndominant: BPFO\n","stderr":"","error":null,"imageCount":0,"durationMs":21,"ranAt":"2026-07-08T21:28:54.329Z"}

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