{"id":"W3047265813","doi":"","title":"Commute Mode and Residential Location Choice","year":2019,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Microdata (statistics); Discrete choice; Mode choice; Business; Geographic information system; Public transport; Mode (computer interface); Location model; Transport engineering; Household income; Neighbourhood (mathematics); Public use; Public economics; Environmental economics; Geography; Economics; Econometrics; Microeconomics; Computer science; Engineering","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":[0.0006498462,0.0001735071,0.0003059317,0.000378951,0.0002876425,0.001248439,0.000409346,0.0006805096,0.01612335],"category_scores_gemma":[0.003217896,0.000238708,0.0005377828,0.0006166938,0.0005055766,0.0005575322,0.0005333527,0.0006393489,0.001074805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009063394,"about_ca_system_score_gemma":0.0004553467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03654799,"about_ca_topic_score_gemma":0.04946643,"domain_scores_codex":[0.9996065,0.0001848768,0.000008194594,0.00006717699,0.00004254518,0.00009071112],"domain_scores_gemma":[0.9988133,0.0007066244,0.0001310628,0.00008799905,0.00006983174,0.0001912942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001356604,0.001011032,0.6801611,0.0001102509,0.0003936542,0.0003834447,0.001526317,0.215911,0.001544479,0.05460076,0.007883371,0.03511796],"study_design_scores_gemma":[0.0003584994,0.0005657796,0.4916027,0.00009300836,0.0002103088,0.0002867664,0.003456397,0.436154,0.0008821544,0.05128712,0.01497599,0.0001272881],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987854,0.00009617087,0.003752724,0.0003068047,0.000007072262,0.00003436175,0.00126078,0.00002480518,0.006663424],"genre_scores_gemma":[0.9923291,0.0000765173,0.0007124355,0.00002305257,0.000002957457,0.0000230917,0.0004791446,0.000008051393,0.006345734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03654799,"threshold_uncertainty_score":0.07267052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09963670218638979,"score_gpt":0.303805084711247,"score_spread":0.2041683825248572,"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."}}