{"id":"W2118689076","doi":"10.3141/2495-04","title":"Joint Econometric Analysis of Temporal and Spatial Flexibility of Activities, Vehicle Type Choice, and Primary Driver Selection","year":2015,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; McGill University","funders":"","keywords":"Flexibility (engineering); Multinomial logistic regression; Travel behavior; Econometric model; Selection (genetic algorithm); Discrete choice; Mixed logit; Econometrics; Computer science; Transport engineering; Logistic regression; Engineering; Economics; Statistics; Mathematics; Artificial intelligence; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003676156,0.000343939,0.0006252391,0.001273134,0.0004179936,0.001263521,0.0007110599,0.0004357006,0.003205227],"category_scores_gemma":[0.009122482,0.000379343,0.001133368,0.002173418,0.0006714903,0.0006504745,0.0008423674,0.0008095229,0.0002741296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002918316,"about_ca_system_score_gemma":0.00313394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3768387,"about_ca_topic_score_gemma":0.3560623,"domain_scores_codex":[0.9983576,0.0007120844,0.00007804574,0.0002651219,0.0002291533,0.0003580534],"domain_scores_gemma":[0.991421,0.005559917,0.001384145,0.0005007539,0.0007178498,0.0004161377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001951122,0.0001313629,0.8215341,0.00004803096,0.0004383259,0.0004034803,0.000308639,0.1516081,0.0004133606,0.008089415,0.0006985763,0.0161316],"study_design_scores_gemma":[0.00002492352,0.0001409781,0.5259379,0.0000160082,0.0001657131,0.00008178811,0.001018287,0.4662872,0.0002490116,0.003889918,0.002135155,0.00005311365],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800692,0.0002635174,0.01637619,0.0002609248,0.00001224089,0.00005398917,0.001203311,0.00004959913,0.001711135],"genre_scores_gemma":[0.9954769,0.00009657927,0.001947889,0.00001503391,0.000007745223,0.00002119213,0.0006483645,0.000004990944,0.001781317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3768387,"threshold_uncertainty_score":0.7492904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1433398766132788,"score_gpt":0.4045697682695689,"score_spread":0.2612298916562902,"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."}}