{"id":"W4382045623","doi":"10.1016/j.tbs.2023.100624","title":"How daily activities and built environment affect health? A latent segmentation-based random parameter logit modeling approach","year":2023,"lang":"en","type":"article","venue":"Travel Behaviour and Society","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Random effects model; Affect (linguistics); Logit; Econometrics; Logistic regression; Segmentation; Discrete choice; Random forest; Mixed logit; Investment (military); Latent variable; Duration (music); Travel behavior; Business; Computer science; Statistics; Transport engineering; Economics; Psychology; Mathematics; Engineering; Medicine; Meta-analysis; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.004260994,0.001225409,0.002237398,0.001611773,0.000758845,0.002721486,0.003590527,0.003047983,0.009181644],"category_scores_gemma":[0.007380839,0.001225635,0.002856918,0.002355721,0.00166904,0.002645867,0.002177419,0.002197713,0.001248716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001794686,"about_ca_system_score_gemma":0.00184003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03161044,"about_ca_topic_score_gemma":0.024091,"domain_scores_codex":[0.9963642,0.002283395,0.0001046663,0.0006524964,0.000133823,0.0004615113],"domain_scores_gemma":[0.9942943,0.004443257,0.0004912915,0.000267979,0.0002819321,0.0002210809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002106176,0.002075403,0.1940569,0.0005724988,0.002929264,0.0009401075,0.003734199,0.5852336,0.00171358,0.1572667,0.006310617,0.04306091],"study_design_scores_gemma":[0.0001318521,0.0002591065,0.01745076,0.00006698018,0.0004920979,0.0001209956,0.0007574018,0.9365871,0.0001896186,0.0421581,0.001709382,0.00007659473],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5441295,0.001020872,0.4394336,0.0041927,0.0001745184,0.0005221199,0.00487034,0.0008770027,0.004779343],"genre_scores_gemma":[0.9720384,0.0003419736,0.01732409,0.0001888591,0.00007591337,0.0004651948,0.001702311,0.00005819243,0.007805025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03161044,"threshold_uncertainty_score":0.06285286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06363606102809782,"score_gpt":0.3037221483336487,"score_spread":0.2400860873055509,"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."}}