{"id":"W2256163264","doi":"","title":"Performance Metrics and Analysis of Transit Network Resilience in Toronto","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Resilience (materials science); Public transport; Transport engineering; Network analysis; Transit (satellite); Work (physics); Computer science; Risk analysis (engineering); Business; Engineering","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":[0.0005930668,0.0004312425,0.0002174009,0.002095375,0.0007133496,0.001006454,0.0004153548,0.0003174123,0.001700256],"category_scores_gemma":[0.003113376,0.0001253494,0.0002444495,0.002899326,0.0007529526,0.0007527755,0.0008605156,0.0002007893,0.00009673608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01220037,"about_ca_system_score_gemma":0.002628499,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6554599,"about_ca_topic_score_gemma":0.6518114,"domain_scores_codex":[0.9996085,0.00006921231,0.00002125323,0.00005993415,0.0001147588,0.0001264418],"domain_scores_gemma":[0.9987488,0.0003423371,0.0002563694,0.00008990859,0.0004168165,0.0001457598],"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":[0.0003111499,0.00003888027,0.1937979,0.0001823783,0.0001253235,0.0006549075,0.001954986,0.7384171,0.00582984,0.02472999,0.002806834,0.03115063],"study_design_scores_gemma":[0.000008373339,0.0001380286,0.3272229,0.00006615817,0.0000784821,0.0001822148,0.004616872,0.6550501,0.003222783,0.00473355,0.004619301,0.00006131095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861325,0.000228925,0.005968415,0.0001920871,0.000007032755,0.00003693978,0.001670791,0.0000713851,0.005691981],"genre_scores_gemma":[0.9985552,0.0000581362,0.0006299376,0.000002088882,9.725542e-7,0.000005067856,0.0003394635,0.000002928265,0.0004061348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3445401,"threshold_uncertainty_score":0.6931382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02246778261894789,"score_gpt":0.3287493508082682,"score_spread":0.3062815681893203,"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."}}