{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.01631001,0.0003931704,0.0006426109,0.001155044,0.005887286,0.0009986911,0.001328744,0.0004554566,0.0003007294],"category_scores_gemma":[0.001886184,0.0004172659,0.0001896248,0.001140284,0.002644076,0.002439288,0.0000282468,0.002239464,0.00004452395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002979936,"about_ca_system_score_gemma":0.0006724675,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2445214,"about_ca_topic_score_gemma":0.5857704,"domain_scores_codex":[0.9892458,0.001444822,0.001388155,0.001398121,0.004192501,0.002330557],"domain_scores_gemma":[0.9944437,0.00133569,0.0003748687,0.0008306184,0.002112559,0.0009025562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004842345,0.0002226197,0.9373545,0.0002522856,0.00003276757,0.0002739826,0.04072213,0.0003200314,0.0002799279,0.002464775,0.0002609952,0.01733173],"study_design_scores_gemma":[0.0016474,0.000137349,0.9770814,0.0004386956,0.00002202317,8.05754e-8,0.01294468,0.0006106687,0.0001622077,0.005177861,0.001363382,0.0004143184],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873173,0.0002939263,0.0002736815,0.002659489,0.0003066342,0.001786367,0.0001349204,0.0001704461,0.007057229],"genre_scores_gemma":[0.9943716,0.0003953315,0.003029503,0.00003639421,0.0004378861,0.0002855531,0.0001177898,0.0000641389,0.001261796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3412489,"threshold_uncertainty_score":0.9998279,"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."}}