{"id":"W757701294","doi":"","title":"Temporal Transferability and Updating of Safety Planning Models","year":2006,"lang":"en","type":"article","venue":"Transportation Research Board 85th Annual MeetingTransportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transferability; Calibration; Context (archaeology); Bayesian probability; Computer science; Sample (material); Statistical model; Sensitivity (control systems); Predictive modelling; Econometrics; Data mining; Machine learning; Statistics; Artificial intelligence; Geography; Engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005222275,0.0004469232,0.0007097179,0.0009981011,0.0007150876,0.00008685418,0.0004641826,0.0003883197,0.0001142102],"category_scores_gemma":[0.00007409718,0.0004617882,0.0002033103,0.001752281,0.0009886259,0.000955463,0.00001003053,0.001702551,0.00001152829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001547298,"about_ca_system_score_gemma":0.0002450859,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007107425,"about_ca_topic_score_gemma":0.006884289,"domain_scores_codex":[0.9924523,0.0006113373,0.001734854,0.0008608605,0.002829246,0.001511408],"domain_scores_gemma":[0.996285,0.0009760028,0.0001073475,0.0004803705,0.001699717,0.000451603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001993361,0.0004963269,0.2942074,0.003286272,0.000235872,0.0001623831,0.01986087,0.6218307,0.007391573,0.03931723,0.003267774,0.007950237],"study_design_scores_gemma":[0.0026881,0.000453048,0.9443702,0.0005393218,0.00005479211,0.000001169426,0.01197809,0.02413298,0.004084525,0.006528339,0.00435349,0.0008159892],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785404,0.0008273075,0.01141249,0.0003358702,0.0001256655,0.001323018,0.0007760082,0.000512394,0.006146797],"genre_scores_gemma":[0.9941555,0.0003546415,0.004262232,0.0000100158,0.0001476747,0.000149585,0.0006479015,0.0001164235,0.0001560766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6501628,"threshold_uncertainty_score":0.9997834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04219145936165196,"score_gpt":0.3280081633461215,"score_spread":0.2858167039844696,"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."}}