{"id":"W602543054","doi":"","title":"Crash Prediction Modeling Down Under: Some Key Findings","year":2009,"lang":"en","type":"article","venue":"Transportation Research Board 88th Annual MeetingTransportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crash; Visibility; Key (lock); Transport engineering; Computer science; Engineering; Forensic engineering; Risk analysis (engineering); Computer security; Business; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01129275,0.001788275,0.001717085,0.00219167,0.001166246,0.004344504,0.002275958,0.001167272,0.007834588],"category_scores_gemma":[0.03190388,0.0006099566,0.002048273,0.002068368,0.001222246,0.007501388,0.00249071,0.002705654,0.001293299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002606616,"about_ca_system_score_gemma":0.003001575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07267356,"about_ca_topic_score_gemma":0.02119939,"domain_scores_codex":[0.9948303,0.001842557,0.000404617,0.001047925,0.001342799,0.0005317327],"domain_scores_gemma":[0.9754688,0.01411476,0.001404254,0.001653864,0.006875405,0.0004829026],"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":[0.0008847128,0.00119142,0.3170124,0.002180967,0.0005818967,0.001625045,0.00618608,0.07437062,0.003139788,0.1113059,0.0280452,0.4534759],"study_design_scores_gemma":[0.00008968791,0.002786847,0.2798568,0.002942295,0.00138209,0.00142018,0.02313835,0.4060476,0.01058784,0.1283848,0.1428706,0.0004928755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5549634,0.03673666,0.2517154,0.05694404,0.001798078,0.001217121,0.006133708,0.001042142,0.08944949],"genre_scores_gemma":[0.9286981,0.01790933,0.02905991,0.003260781,0.001134431,0.0002573392,0.002687329,0.0002916699,0.0167011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07267356,"threshold_uncertainty_score":0.1445011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0384686497223236,"score_gpt":0.3222159505151309,"score_spread":0.2837473007928073,"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."}}