{"id":"W2014037981","doi":"10.4271/2014-01-0569","title":"A Neural Network Approach for Predicting Collision Severity","year":2014,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Traffic and Road Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Transport Canada","keywords":"Collision; Artificial neural network; Computer science; Artificial intelligence; Machine learning; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005812927,0.0008026141,0.0006461475,0.001491211,0.000385362,0.0006986261,0.001018194,0.001330096,0.002076357],"category_scores_gemma":[0.001764118,0.0004537855,0.0004980778,0.001213573,0.0002201326,0.0008650738,0.0004420288,0.001001249,0.0006415154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007847213,"about_ca_system_score_gemma":0.0005684616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03131843,"about_ca_topic_score_gemma":0.02540593,"domain_scores_codex":[0.9997706,0.00003011469,0.00002031827,0.00008194412,0.00005959463,0.00003748095],"domain_scores_gemma":[0.9993278,0.0003220708,0.00005311688,0.00002924144,0.0002409543,0.00002684293],"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":[0.0002484396,0.000357783,0.007261542,0.00004898751,0.0001007703,0.0001149889,0.00002506627,0.7442052,0.003769138,0.0009512107,0.001851759,0.2410651],"study_design_scores_gemma":[0.000002212384,0.0000107991,0.0005378919,0.000002294155,0.000006179957,0.000004557232,0.000002366521,0.9989008,0.0002345791,0.0002492392,0.00004651561,0.000002606826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2978433,0.001881918,0.6901448,0.0005209331,0.0004135059,0.0001521107,0.00110494,0.00160344,0.006335204],"genre_scores_gemma":[0.9317666,0.0003997864,0.06154429,0.00007905062,0.0001176634,0.00008845526,0.0006168796,0.00002879335,0.00535852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03131843,"threshold_uncertainty_score":0.06227225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009855030905343946,"score_gpt":0.2165271936132415,"score_spread":0.2066721627078976,"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."}}