{"id":"W578532935","doi":"","title":"Analyzing Car Ownership in Two Quebec Metropolitan Regions: Comparison of Latent Ordered and Unordered Response Models","year":2013,"lang":"en","type":"article","venue":"Transportation Research Board 92nd Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latent class model; Multinomial logistic regression; Econometrics; Car ownership; Context (archaeology); Population; Class (philosophy); Latent variable; Metropolitan area; Geography; Logistic regression; Estimation; Economics; Demography; Statistics; Computer science; Political science; Mathematics; Sociology; Public transport; Artificial intelligence","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":[0.008288383,0.0009341954,0.0007853583,0.001487802,0.001034401,0.002236725,0.002580039,0.0009145788,0.003224754],"category_scores_gemma":[0.01583029,0.0003672484,0.001581862,0.00251045,0.0009554888,0.0008452438,0.001107002,0.001387574,0.0003475962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0129703,"about_ca_system_score_gemma":0.007753702,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9360949,"about_ca_topic_score_gemma":0.9142055,"domain_scores_codex":[0.996292,0.002567685,0.00008907344,0.0004537277,0.0001951338,0.0004023661],"domain_scores_gemma":[0.9814875,0.01373832,0.001173472,0.0009114778,0.002193399,0.0004958664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001554973,0.001111762,0.6185046,0.0002814961,0.001457148,0.0004643413,0.003432037,0.3012668,0.0008235621,0.01890955,0.006382606,0.04581115],"study_design_scores_gemma":[0.0001038467,0.000178442,0.2073025,0.00008391227,0.0002104713,0.00004147162,0.003192854,0.7835215,0.0002570704,0.002678848,0.002342314,0.00008673221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851058,0.000329472,0.01003465,0.0005989816,0.0000204968,0.0001138658,0.002020897,0.00006967235,0.001706189],"genre_scores_gemma":[0.9900252,0.000171741,0.004632282,0.00007317705,0.000009069411,0.0001100265,0.002964253,0.00002202653,0.001992251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06390506,"threshold_uncertainty_score":0.1285627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1172958184864629,"score_gpt":0.4376679322780165,"score_spread":0.3203721137915536,"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."}}