{"id":"W3206238941","doi":"10.1080/19439962.2021.1988787","title":"An integrated clustering and Bayesian approach to investigate the severity of pedestrian collisions at highway-railway grade crossings collisions","year":2021,"lang":"en","type":"article","venue":"Journal of Transportation Safety & Security","topic":"Traffic and Road Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Collision; Pedestrian; Cluster analysis; Transport engineering; Bayesian probability; Warning system; Computer science; Bayes' theorem; Engineering; Computer security; Machine learning; Artificial intelligence","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":[0.002453239,0.0007982711,0.0009088053,0.007665591,0.0007984096,0.00136232,0.001040145,0.0009684589,0.001193078],"category_scores_gemma":[0.007048971,0.0004482869,0.001408913,0.004223458,0.0003217063,0.001070672,0.001292731,0.0007766837,0.0003398021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001388251,"about_ca_system_score_gemma":0.002331189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04045343,"about_ca_topic_score_gemma":0.04933304,"domain_scores_codex":[0.997646,0.0007641754,0.0001842133,0.0005031918,0.0006729003,0.0002295061],"domain_scores_gemma":[0.9971929,0.0009504523,0.0006091649,0.000202438,0.0008994815,0.0001455969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006130632,0.0009584216,0.573238,0.0003130207,0.001381079,0.000299599,0.001283967,0.2269301,0.003067094,0.008692686,0.004627948,0.1785951],"study_design_scores_gemma":[0.00002583948,0.0002708429,0.2036642,0.0000915258,0.0003216922,0.000240457,0.001427555,0.7832457,0.0009734739,0.00664684,0.002966703,0.0001251845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6141279,0.0008042946,0.3738823,0.0005152372,0.00009028627,0.0007015062,0.002865522,0.0005907838,0.006422274],"genre_scores_gemma":[0.9072853,0.0002783745,0.08817592,0.00007090765,0.00003709162,0.0002358625,0.002493117,0.00003499784,0.00138831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04045343,"threshold_uncertainty_score":0.08043593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01279833608239226,"score_gpt":0.2358621519857231,"score_spread":0.2230638159033309,"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."}}