{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001881176,0.0001657697,0.0002227115,0.0001074604,0.00007269392,0.00004294818,0.0001268261,0.0001199532,0.00001509582],"category_scores_gemma":[0.0001456504,0.0001805254,0.00004244895,0.0001536302,0.0000247104,0.000261914,0.000021297,0.0001240046,0.00004295976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001202693,"about_ca_system_score_gemma":0.000007256865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001606573,"about_ca_topic_score_gemma":0.00002562448,"domain_scores_codex":[0.9988165,0.00006656043,0.0004503524,0.0002463001,0.0001037197,0.000316501],"domain_scores_gemma":[0.9994267,0.0001622614,0.00004861363,0.0002691949,0.00003393898,0.00005927617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002228698,0.00001857143,0.0002497736,0.0000311994,0.00001087822,7.739949e-7,0.0001452505,0.8757958,0.1193662,0.000526871,0.00003436431,0.003818107],"study_design_scores_gemma":[0.0006126286,0.00003416974,0.003666301,0.00002591346,0.00001282203,0.000001930264,0.0001196587,0.9775764,0.0164037,0.00118062,0.0001473845,0.0002184128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04040166,0.0001013358,0.9581644,0.0001425665,0.0004039787,0.0006479778,0.000003750694,0.00007596994,0.00005835206],"genre_scores_gemma":[0.6113365,0.0001006587,0.3880132,0.0001067661,0.00005172708,0.0001180283,0.00007766939,0.00005673404,0.0001387113],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5709348,"threshold_uncertainty_score":0.7361614,"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."}}