{"id":"W7096051914","doi":"","title":"Recommended Citation Tang, Zhongyuan, "Modeling Commuting Behaviour in Canadian Metropolitan Areas: Investigation of Factors and Methods with Micro Data " (2011). Electronic Theses and Dissertations. Paper 5392. Modeling Commuting Behaviour in Canadian Me","year":2011,"lang":"en","type":"article","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Windsor; Permission; Metropolitan area; License; Citation","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":[0.001034882,0.0005284409,0.0008566031,0.004615794,0.002445163,0.003348098,0.002375381,0.0006945659,0.03866892],"category_scores_gemma":[0.01196504,0.0003564223,0.0004752084,0.01971158,0.00053961,0.001731379,0.001062459,0.001038019,0.003787672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01348521,"about_ca_system_score_gemma":0.02271163,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9474146,"about_ca_topic_score_gemma":0.9559584,"domain_scores_codex":[0.9995548,0.00004833797,0.00002343865,0.00006358435,0.0002535237,0.00005619337],"domain_scores_gemma":[0.9970672,0.0005480087,0.0001434462,0.0001172158,0.001907927,0.0002162937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004134804,0.00003434231,0.0186175,0.00107875,0.00008017683,0.00008772677,0.001411989,0.008557825,0.000135639,0.007584116,0.8493015,0.1130692],"study_design_scores_gemma":[0.00005828667,0.00005186324,0.08812762,0.001797617,0.0003139444,0.0001256437,0.004445653,0.02470561,0.000507204,0.00650687,0.8732194,0.000140219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.09137894,0.09508546,0.06579807,0.1143604,0.03859864,0.003498493,0.3599415,0.0017776,0.2295609],"genre_scores_gemma":[0.4008056,0.1241997,0.03815034,0.003180088,0.003313671,0.001475969,0.1182713,0.0007457652,0.3098575],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05258536,"threshold_uncertainty_score":0.1293604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.111061332400797,"score_gpt":0.3593570246943877,"score_spread":0.2482956922935907,"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."}}