{"id":"W7018559588","doi":"","title":"Derailment Prediction Models for Canadas Rail Network","year":2020,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Transport Canada","keywords":"Derailment; Process (computing); Predictive modelling; Logistic regression; Risk assessment; Statistical model","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0009556708,0.0006262312,0.0003581413,0.0009069346,0.0005581351,0.0009220651,0.001040694,0.0004852902,0.003990481],"category_scores_gemma":[0.001952589,0.0002534314,0.0006694871,0.0007851607,0.0002139263,0.0004393315,0.0005619164,0.0008408978,0.0006759553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006188689,"about_ca_system_score_gemma":0.006002227,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7839683,"about_ca_topic_score_gemma":0.7116835,"domain_scores_codex":[0.9997445,0.00004888305,0.00001093623,0.00006730355,0.00006069599,0.0000676999],"domain_scores_gemma":[0.9991705,0.0003002353,0.00006897935,0.000023312,0.0003947927,0.00004215787],"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":[0.00008821062,0.00006769782,0.02730316,0.00006416058,0.0000554179,0.00008234281,0.0001324491,0.9259677,0.0003279395,0.004169802,0.005768276,0.03597276],"study_design_scores_gemma":[0.00000312029,0.00001022177,0.003157394,0.00001025745,0.00001132754,0.000006046676,0.00005276121,0.9950959,0.00007529264,0.0006163368,0.0009564355,0.000004797266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.837144,0.001786171,0.1204485,0.002755139,0.0001270899,0.000260131,0.009267875,0.001672613,0.02653845],"genre_scores_gemma":[0.9595161,0.0006583223,0.01979421,0.0000942955,0.00002303431,0.0001146842,0.005241408,0.00006258068,0.01449536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2160317,"threshold_uncertainty_score":0.4346079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01520650749191879,"score_gpt":0.1486443524001482,"score_spread":0.1334378449082294,"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."}}