{"id":"W2087005010","doi":"10.1080/01691864.2013.763743","title":"A victim identification methodology for rescue robots operating in cluttered USAR environments","year":2013,"lang":"en","type":"article","venue":"Advanced Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Urban search and rescue; Artificial intelligence; Computer science; Silhouette; Computer vision; Support vector machine; Robot; Classifier (UML); Robustness (evolution); Identification (biology); Workload; Machine learning; Mobile robot","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000514933,0.0006494531,0.0005763093,0.001632819,0.000494475,0.0005939538,0.001105634,0.0009245384,0.001004992],"category_scores_gemma":[0.001387457,0.0003655865,0.0005020995,0.0004881921,0.0005479758,0.0009180274,0.0007746561,0.0005317838,0.0007875842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002193247,"about_ca_system_score_gemma":0.0006849272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006949086,"about_ca_topic_score_gemma":0.00121006,"domain_scores_codex":[0.9994642,0.00007717148,0.00003472518,0.0001435628,0.0002418986,0.00003854596],"domain_scores_gemma":[0.9991435,0.0001285212,0.000208099,0.0001196021,0.0003637696,0.00003655905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009006484,0.000245763,0.003892826,0.0003516835,0.00009604383,0.0004452251,0.0003154404,0.03687444,0.2354811,0.003135711,0.002527336,0.7165445],"study_design_scores_gemma":[0.00002735824,0.0008658118,0.008620983,0.0000816625,0.00007724104,0.003537324,0.0005967607,0.8218614,0.1492157,0.003567503,0.01142072,0.0001274714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009389799,0.00007629918,0.9894022,0.00003827639,0.00001646646,0.0001042105,0.00002458782,0.0005851568,0.000363042],"genre_scores_gemma":[0.1406154,0.000227113,0.8571275,0.00008603721,0.0000235381,0.0001857879,0.0001212812,0.00005818238,0.00155514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001632819,"threshold_uncertainty_score":0.00336206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02982454303080895,"score_gpt":0.2658901006521466,"score_spread":0.2360655576213376,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}