{"id":"W4360603470","doi":"10.1016/j.tust.2023.105079","title":"Modeling interactions between the Montreal subway and other urban transportation modes at the station level","year":2023,"lang":"en","type":"article","venue":"Tunnelling and Underground Space Technology","topic":"Underground infrastructure and sustainability","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Destinations; Mode (computer interface); Transport engineering; Categorical variable; Space (punctuation); Computer science; Operations research; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002083698,0.0001765197,0.0001648905,0.000159818,0.0005001811,0.00006953964,0.0001119766,0.0001468448,0.000009277366],"category_scores_gemma":[0.00001793963,0.0001196428,0.00003455103,0.0003798354,0.0001944321,0.0001469164,0.00002232194,0.0003278164,0.000004561075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009053607,"about_ca_system_score_gemma":0.0000132412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006825702,"about_ca_topic_score_gemma":0.004526163,"domain_scores_codex":[0.9991352,0.00003003737,0.0002130868,0.000230794,0.0001012518,0.0002896575],"domain_scores_gemma":[0.9994368,0.0002152259,0.00003180931,0.0002404377,0.00003919055,0.00003654929],"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":[0.00004994907,0.00002120519,0.04508849,0.0001906089,0.0003571891,0.000007517254,0.0100951,0.8653726,0.003166687,0.05484813,0.002017356,0.01878515],"study_design_scores_gemma":[0.0003595974,0.00004647552,0.01051217,0.00002296339,0.0001001636,0.00001385218,0.01429516,0.7792596,0.0004104494,0.1915136,0.003173783,0.0002922702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8581328,0.0003991199,0.1373569,0.002985633,0.00006862914,0.0001771519,0.00002614848,0.0005238508,0.0003297891],"genre_scores_gemma":[0.9988113,0.0002691509,0.0001905763,0.00003391733,0.0000640717,0.00002853892,0.00002800777,0.00003195024,0.0005424555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1406785,"threshold_uncertainty_score":0.4878891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133318787581122,"score_gpt":0.2386344928216161,"score_spread":0.2173013049458049,"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."}}