{"id":"W587045215","doi":"10.1007/s11116-017-9765-3","title":"How household transportation expenditures have evolved in Canada: a long term perspective","year":2017,"lang":"en","type":"article","venue":"Transportation","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Economics; Public economics; Public transport; Econometric model; Econometric analysis; Affect (linguistics); Value of time; Perspective (graphical); Discrete choice; Vehicle miles of travel; Econometrics; Travel time; Transport engineering; Engineering","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":[0.001412796,0.0003768363,0.000615691,0.002758669,0.004996969,0.006399287,0.001926947,0.001621107,0.006422173],"category_scores_gemma":[0.004413839,0.0003081012,0.0008848369,0.01096147,0.002283025,0.002504172,0.002311473,0.002886089,0.0002935051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1076514,"about_ca_system_score_gemma":0.0946466,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9972417,"about_ca_topic_score_gemma":0.998522,"domain_scores_codex":[0.9981097,0.0001268889,0.00007461435,0.0001902029,0.000446485,0.001052182],"domain_scores_gemma":[0.9941981,0.0002677375,0.0005258013,0.00012649,0.003626487,0.001255406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003440015,0.000214977,0.8625605,0.0003757831,0.000548336,0.0006023183,0.01234355,0.003617045,0.0007487449,0.03996003,0.02192097,0.05676378],"study_design_scores_gemma":[0.000005478935,0.00003103046,0.9530839,0.0002299769,0.00007788136,0.00006932946,0.01311249,0.0006744446,0.0001283579,0.001006935,0.03152216,0.00005806703],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8951181,0.0155569,0.0005607464,0.0422856,0.0002868303,0.00006328242,0.01651281,0.00004013781,0.02957556],"genre_scores_gemma":[0.9812986,0.005602044,0.0004018108,0.001406648,0.00006125752,0.00001793038,0.003128381,0.0000294949,0.008053701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1076514,"threshold_uncertainty_score":0.7810695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03789175180858625,"score_gpt":0.2945246342178068,"score_spread":0.2566328824092206,"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."}}