{"id":"W3145758943","doi":"","title":"Estimating Latent Cycling and Walking Trips in Montreal","year":2017,"lang":"en","type":"article","venue":"Transportation Research Board 96th Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Cycling; TRIPS architecture; Latent class model; Geography; Environmental science; Transport engineering; Statistics; Mathematics; Engineering; Forestry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001620984,0.0008341793,0.0005003514,0.001226949,0.0009751864,0.001630725,0.001629832,0.0007532279,0.004675556],"category_scores_gemma":[0.005663543,0.0005729101,0.0009404827,0.001688521,0.0007056126,0.0006856946,0.001040486,0.001108883,0.0006565809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01008523,"about_ca_system_score_gemma":0.006982504,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9651918,"about_ca_topic_score_gemma":0.9707276,"domain_scores_codex":[0.9994162,0.0001897134,0.00001882093,0.0002017552,0.0000545506,0.0001189557],"domain_scores_gemma":[0.9988356,0.0004343502,0.0001337059,0.0001208563,0.0002852917,0.0001901861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004750614,0.0003128741,0.8323629,0.00007247227,0.0007991091,0.0001626448,0.0006263157,0.1079149,0.0009915194,0.005857772,0.009307386,0.041117],"study_design_scores_gemma":[0.00008160226,0.00007929259,0.5625171,0.00006499884,0.0001430176,0.00002191249,0.0007489363,0.4300488,0.0002675737,0.001721221,0.004220073,0.00008536012],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821973,0.0005227851,0.005571646,0.0007869567,0.00003178234,0.00006942473,0.008569343,0.0002581242,0.001992738],"genre_scores_gemma":[0.9812908,0.0001810754,0.003831184,0.00005239652,0.00001932016,0.00003904484,0.01156861,0.00003925898,0.00297844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03480822,"threshold_uncertainty_score":0.07317376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08883879546757519,"score_gpt":0.4317298770907972,"score_spread":0.342891081623222,"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."}}