{"id":"W4408146128","doi":"10.1109/icmla61862.2024.00146","title":"Pedestrian Detection: An Explainable Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Pedestrian; Computer science; Pedestrian detection; Computer vision; Artificial intelligence; Transport engineering; Engineering","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":[0.00007701272,0.00007285918,0.00006103488,0.00006867301,0.00005412683,0.0000306133,0.00007956947,0.0001162937,0.0001371488],"category_scores_gemma":[0.000001860633,0.00006687218,0.00002352659,0.0001631994,0.00001975555,0.0001773079,0.00001058305,0.0001738659,0.0001460305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003652019,"about_ca_system_score_gemma":0.000008361501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009500589,"about_ca_topic_score_gemma":0.00001439583,"domain_scores_codex":[0.9996138,0.000005447992,0.00007710086,0.0001236944,0.00003386703,0.0001461364],"domain_scores_gemma":[0.9998,0.000008355812,0.000001954126,0.0001516872,0.000004330415,0.00003373202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001341119,0.00008569143,0.0001899239,0.0005460639,0.000180708,0.0001242638,0.001027365,0.03078678,0.01098414,0.1297844,0.002969903,0.8233074],"study_design_scores_gemma":[0.00009726,0.00005488527,0.0002504672,0.000005486819,0.00001184434,0.00009698656,0.0004266048,0.8907345,0.02067171,0.002709045,0.08471531,0.0002258944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.171732,0.001137968,0.5135213,0.0001000657,0.000506699,0.0001454754,0.000001975588,0.0120863,0.3007682],"genre_scores_gemma":[0.9968576,0.00002255087,0.001636275,0.00001037212,0.00007167362,0.00002191347,0.000003015505,0.00001916177,0.001357387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8599477,"threshold_uncertainty_score":0.2726969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007842455387717918,"score_gpt":0.1972307937826798,"score_spread":0.1893883383949619,"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."}}