{"id":"W38009008","doi":"10.1287/trsc.2015.0597","title":"A Dynamic Formulation for Car Ownership Modeling","year":2015,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Context (archaeology); Computer science; Quality (philosophy); Discrete choice; Consumer choice; Set (abstract data type); Frame (networking); Product (mathematics); Consumer behaviour; Operations research; Mathematical optimization; Microeconomics; Economics; Marketing; Business; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001466614,0.00007504824,0.00008542729,0.0001730952,0.0005819084,0.00008469333,0.0001973181,0.00005952817,0.000009469912],"category_scores_gemma":[0.0001525645,0.00007636572,0.00003993357,0.0007825844,0.0001249014,0.001062001,5.86487e-7,0.00004931138,0.00000679517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001203034,"about_ca_system_score_gemma":0.0006806031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004355937,"about_ca_topic_score_gemma":0.00236628,"domain_scores_codex":[0.9985644,0.00001994723,0.0002384269,0.0002559297,0.000642902,0.0002784138],"domain_scores_gemma":[0.9989139,0.0000426086,0.00008284796,0.00009607994,0.0006579109,0.0002066332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000691969,0.00002915675,0.007931963,0.00001456906,0.000003821295,8.741995e-7,0.08489602,0.7850823,0.0004371936,0.1193598,0.0000442417,0.002130861],"study_design_scores_gemma":[0.001103574,0.0000653727,0.01207639,0.00003226619,0.00003954635,2.211376e-7,0.01686005,0.9563414,0.0001337081,0.009490435,0.003527839,0.0003291818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3860602,0.00002254516,0.6108592,0.0005019638,0.0002851814,0.0003475033,0.0000213035,0.0001594533,0.001742669],"genre_scores_gemma":[0.9749126,0.00000745304,0.02448861,0.00007399647,0.00003078694,0.00003223377,0.0001298253,0.000007977888,0.0003164877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5888524,"threshold_uncertainty_score":0.4475628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08848845493086999,"score_gpt":0.3605359699050026,"score_spread":0.2720475149741327,"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."}}