{"id":"W2807896297","doi":"10.1139/cjce-2017-0186","title":"Improved multiple discreet-continuous extreme value model","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; King Abdulaziz University; University of Toronto","keywords":"Flexibility (engineering); Constant (computer programming); Model parameter; Duration (music); Computer science; Value (mathematics); Extreme value theory; Econometrics; Statistics; Mathematics; Data mining; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001572827,0.0006517982,0.001249752,0.0006752943,0.0002768993,0.001722431,0.002556102,0.00132888,0.003707116],"category_scores_gemma":[0.003704616,0.0004116395,0.001432008,0.001262189,0.0005311273,0.001149094,0.001020427,0.002103836,0.0004985261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009560234,"about_ca_system_score_gemma":0.0009046945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009063235,"about_ca_topic_score_gemma":0.004985294,"domain_scores_codex":[0.9988698,0.000412109,0.00006711634,0.0003172198,0.000195297,0.000138404],"domain_scores_gemma":[0.9985456,0.0009043386,0.0001614856,0.0001040955,0.0002158817,0.00006847559],"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":[0.00005465991,0.00002920026,0.001203923,0.00004085418,0.00003861491,0.0000664628,0.00003387198,0.9800289,0.0002738636,0.007766793,0.000471994,0.009990855],"study_design_scores_gemma":[0.000004968353,0.000008810763,0.0001850961,0.000003013234,0.000004480245,0.000008882449,0.000003667363,0.9964504,0.00005305665,0.003018632,0.0002550491,0.000003960327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04189998,0.0005113611,0.9514049,0.0004208384,0.00009098961,0.0000668621,0.00103621,0.0003725688,0.004196393],"genre_scores_gemma":[0.8924358,0.0004344929,0.09686211,0.0001649308,0.00007777239,0.0002698489,0.001588743,0.00008364251,0.008082583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009063235,"threshold_uncertainty_score":0.01802099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609725514808471,"score_gpt":0.2175405769767383,"score_spread":0.2014433218286536,"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."}}