{"id":"W2111629719","doi":"10.1007/s11116-015-9588-z","title":"Analysis of vehicle ownership evolution in Montreal, Canada using pseudo panel analysis","year":2015,"lang":"en","type":"article","venue":"Transportation","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Ministère des Transports","keywords":"Car ownership; Binary logit model; Econometrics; Nested logit; License; Logit; Panel data; Econometric model; Ordered logit; Econometric analysis; Demographic economics; Logistic regression; Variables; Variable (mathematics); Geography; Transport engineering; Economics; Statistics; Mathematics; Computer science; Public transport; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005591301,0.00008077512,0.0003054229,0.0003923097,0.00007159141,0.000009488654,0.000114732,0.00007228956,0.00008033405],"category_scores_gemma":[0.00002264165,0.00008184914,0.0001403096,0.004502176,0.00007503015,0.0002651028,6.550176e-7,0.00006341648,3.45995e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004701645,"about_ca_system_score_gemma":0.0007467138,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.99249,"about_ca_topic_score_gemma":0.9996647,"domain_scores_codex":[0.9985934,0.00009432004,0.0004003848,0.0002109097,0.0005017574,0.0001991594],"domain_scores_gemma":[0.9993728,0.00003715854,0.0001649341,0.000141124,0.0001678483,0.0001160787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002925145,0.00003408389,0.9245643,0.000006036895,0.0002801306,0.000004096413,0.006214026,0.06854269,0.00006221762,0.0001388146,0.000004510023,0.0001198554],"study_design_scores_gemma":[0.0001965776,0.000005349371,0.9750999,0.000003892188,0.002416548,2.728978e-9,0.004085291,0.01790749,0.00002753695,0.0001569908,0.00001044693,0.00008995552],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968899,0.00006315424,0.00233433,0.00006756969,0.00004734661,0.0001018881,0.00006520785,0.00001569631,0.000414836],"genre_scores_gemma":[0.9995812,0.000003400684,0.0001050915,0.00001291491,0.00001808648,0.000002916001,0.0002097963,0.000003677995,0.00006289521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0506352,"threshold_uncertainty_score":0.3337711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06066283374302327,"score_gpt":0.2987155175945724,"score_spread":0.2380526838515491,"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."}}