{"id":"W4412137856","doi":"10.1080/03081060.2025.2527323","title":"Modernizing Montreal’s household travel survey: adapting to evolving travel trends and technological shifts","year":2025,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Travel behavior; Transport engineering; Vehicle miles of travel; Engineering; Regional science; Geography; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006886525,0.0001853432,0.0003257007,0.0007030107,0.0006844921,0.00006423051,0.0002130069,0.0004248606,0.00001339995],"category_scores_gemma":[0.0001197863,0.0001861558,0.00003144458,0.001304965,0.000478008,0.0001750785,0.000009306451,0.0003347844,4.60826e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002626886,"about_ca_system_score_gemma":0.00005734677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003694311,"about_ca_topic_score_gemma":0.01368582,"domain_scores_codex":[0.9984975,0.00005335213,0.0003591923,0.0005205914,0.0001658925,0.0004034973],"domain_scores_gemma":[0.9994432,0.0001546492,0.00008087014,0.0001583772,0.00006700838,0.00009588499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000387957,0.00004186856,0.925087,0.00002382501,0.00002492449,0.00001994806,0.003210225,0.00003755689,0.0005943038,0.009400022,0.00006985225,0.06145162],"study_design_scores_gemma":[0.0003437771,0.00003481299,0.9902663,0.00009244731,0.00003648844,3.375618e-7,0.004744524,0.0001952366,0.0002489238,0.003715608,0.0001267492,0.0001948364],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796863,0.001069451,0.01274049,0.002556915,0.00006635116,0.0001728817,0.00006275347,0.0005155058,0.003129387],"genre_scores_gemma":[0.9983197,0.00003341048,0.001050653,0.00007703492,0.00001273535,0.0000270109,0.00004101853,0.00001181845,0.0004265579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0651792,"threshold_uncertainty_score":0.763701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04116264578458948,"score_gpt":0.3046008978114259,"score_spread":0.2634382520268364,"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."}}