{"id":"W4403511942","doi":"10.1109/iv64223.2024.00032","title":"Visual Analytics of Motor Vehicle Accidents","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual analytics; Computer science; Analytics; Visualization; Automotive engineering; Human–computer interaction; Data science; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0006979746,0.0007884394,0.0003058591,0.004804075,0.0003587476,0.002066683,0.0004506147,0.0003859856,0.003274926],"category_scores_gemma":[0.004072292,0.000196787,0.0004032672,0.002775979,0.00031035,0.001274143,0.001500861,0.0004413928,0.0006607758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004933707,"about_ca_system_score_gemma":0.0006583208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008471405,"about_ca_topic_score_gemma":0.008353634,"domain_scores_codex":[0.9993787,0.0001447976,0.00005053892,0.0001016771,0.0002620771,0.00006220213],"domain_scores_gemma":[0.9976935,0.0009172967,0.0003227327,0.0002284357,0.0007309461,0.0001069624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001139762,0.0002683194,0.05155492,0.001806887,0.0003440581,0.001321123,0.01145258,0.09472733,0.02485889,0.03118547,0.06443921,0.7169015],"study_design_scores_gemma":[0.0001049149,0.0003292846,0.07697459,0.0009580375,0.0002265763,0.001262049,0.01440322,0.5784289,0.03411156,0.09269498,0.2001999,0.0003059538],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2966535,0.004285629,0.6135603,0.003277997,0.0003961123,0.0005316252,0.02667682,0.02383902,0.03077895],"genre_scores_gemma":[0.8341972,0.002245188,0.1486423,0.0001966266,0.0001683584,0.0001818828,0.01012545,0.0005652785,0.00367774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008471405,"threshold_uncertainty_score":0.01684415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006492005113103333,"score_gpt":0.235165490162861,"score_spread":0.2286734850497576,"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."}}