{"id":"W2285175548","doi":"","title":"Modelling Disruption Duration for Toronto’s Subway System: An Empirical Investigation Using Lognormal Regression and Hazard Models","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Duration (music); Incident management; Context (archaeology); Hazard; Transport engineering; Regression analysis; Public transport; Computer science; Engineering; Statistics; Geography; Mathematics; Computer security","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":[0.004070473,0.0004711354,0.0004921388,0.0006539547,0.001039488,0.0002131097,0.0003427111,0.0004778763,0.00002311199],"category_scores_gemma":[0.0001053685,0.0003832641,0.0001440663,0.0006270987,0.0004500744,0.003716191,0.00001095861,0.0006786917,0.000004843373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008222175,"about_ca_system_score_gemma":0.0002556704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003010359,"about_ca_topic_score_gemma":0.006518766,"domain_scores_codex":[0.9936525,0.0005441601,0.001219816,0.0009647146,0.002112772,0.001506093],"domain_scores_gemma":[0.9954765,0.0005667534,0.0001607413,0.0004566962,0.002646999,0.0006922914],"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":[0.002449186,0.0001400712,0.05389692,0.003424168,0.000202223,0.00008137384,0.02951345,0.7501578,0.1303824,0.01684854,0.001194842,0.01170893],"study_design_scores_gemma":[0.003074014,0.0008194548,0.04043251,0.001883848,0.00009480233,0.000003940983,0.01763413,0.9111549,0.01822146,0.004865519,0.0007606339,0.001054803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7475725,0.0001973717,0.2495441,0.0001868236,0.0003087216,0.001418649,0.000265731,0.0003917104,0.0001144434],"genre_scores_gemma":[0.9788787,0.0004538764,0.01902611,0.00001362392,0.0005584248,0.0004910988,0.0003482535,0.00014679,0.00008307061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2313063,"threshold_uncertainty_score":0.999862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09354527495738207,"score_gpt":0.3747114063874072,"score_spread":0.2811661314300251,"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."}}