{"id":"W1993597627","doi":"10.3141/2019-25","title":"Comparison of Alternative Methods for Identifying Sites with High Proportion of Specific Accident Types","year":2007,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sheridan College; Toronto Metropolitan University","funders":"Federal Highway Administration","keywords":"Ranking (information retrieval); Accident (philosophy); Identification (biology); Bayes' theorem; Statistics; Computer science; Transport engineering; Set (abstract data type); Engineering; Mathematics; Bayesian probability; Machine learning","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":[0.03311133,0.001556236,0.001450932,0.007662557,0.0005733381,0.001691878,0.002492142,0.001790829,0.001758199],"category_scores_gemma":[0.09501498,0.0006395461,0.001854218,0.003525652,0.001178149,0.002444635,0.001827217,0.001355108,0.0004470993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001591146,"about_ca_system_score_gemma":0.001918069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00814754,"about_ca_topic_score_gemma":0.008230771,"domain_scores_codex":[0.9754171,0.01477057,0.001643668,0.002493575,0.005213264,0.0004617619],"domain_scores_gemma":[0.7750171,0.1989933,0.007081814,0.007697701,0.0102567,0.0009533885],"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":[0.006523734,0.001314564,0.1735388,0.001939911,0.002379013,0.0002260356,0.001032785,0.1598671,0.005112023,0.009077763,0.002301666,0.6366866],"study_design_scores_gemma":[0.0007231286,0.002359476,0.08061668,0.0001731014,0.0005852316,0.0005624778,0.0007040187,0.8965546,0.008000519,0.007315522,0.002117612,0.0002875162],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3994479,0.002082934,0.5905321,0.0003586805,0.0001978826,0.0009086502,0.001455638,0.001688879,0.003327305],"genre_scores_gemma":[0.5767489,0.000817581,0.4179294,0.0001167271,0.0001073162,0.0006034556,0.002240759,0.0002066184,0.001229321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03311133,"threshold_uncertainty_score":0.1751116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1647859820529594,"score_gpt":0.471218260366059,"score_spread":0.3064322783130997,"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."}}