{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001799731,0.0006048863,0.0003002964,0.001740452,0.0004900075,0.0009375929,0.001108425,0.0004737856,0.002244554],"category_scores_gemma":[0.007128108,0.0002859102,0.0007449943,0.002208147,0.00055193,0.000785737,0.0006718993,0.0009742777,0.0002666952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006504688,"about_ca_system_score_gemma":0.002788326,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6235813,"about_ca_topic_score_gemma":0.572749,"domain_scores_codex":[0.9993739,0.000191137,0.00004120844,0.0001041991,0.0001489911,0.0001405929],"domain_scores_gemma":[0.9931219,0.004318235,0.001286484,0.0003511496,0.0006449702,0.0002772177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003006183,0.0002323789,0.5401548,0.0001859156,0.0002835876,0.0005904965,0.001737067,0.4292929,0.0008125848,0.004825648,0.002688285,0.01889573],"study_design_scores_gemma":[0.00001637821,0.0001501365,0.3500002,0.00003769057,0.00009581214,0.000113266,0.001742181,0.6442421,0.0004151758,0.0009457447,0.002195062,0.00004627132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926841,0.0002688607,0.004809578,0.0001402022,0.000006791459,0.00003216981,0.001224153,0.00004245362,0.000791819],"genre_scores_gemma":[0.9960359,0.0002579687,0.001261007,0.00000830411,0.000004476076,0.00002037683,0.001398844,0.000008906783,0.001004271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3764187,"threshold_uncertainty_score":0.7572711,"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."}}