{"id":"W2107127880","doi":"10.1007/s11116-015-9659-1","title":"Capturing, measuring and responding to changes that influence travel behavior","year":2015,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Destinations; Travel behavior; Focus (optics); Work (physics); Journey to work; Behaviour change; Service (business); Transport engineering; Data science; Regional science; Computer science; Operations research; Sociology; Marketing; Psychology; Engineering; Political science; Public transport; Tourism; Business","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001007087,0.0005034025,0.0004550607,0.000930317,0.0002567336,0.00166619,0.0004173679,0.0007266354,0.0008000024],"category_scores_gemma":[0.006792969,0.0002769954,0.0002845687,0.001536665,0.0003488145,0.001062088,0.0004132349,0.0007374599,0.0001907191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000583137,"about_ca_system_score_gemma":0.000952331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01230229,"about_ca_topic_score_gemma":0.01664424,"domain_scores_codex":[0.9990427,0.0003871515,0.00005194217,0.0002535577,0.0001924269,0.00007213814],"domain_scores_gemma":[0.9973384,0.001528701,0.0006172363,0.0002088104,0.0002134369,0.00009347349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002949834,0.0008686821,0.6892414,0.0003885023,0.0003822604,0.0001053702,0.001206578,0.0692079,0.03722782,0.002935568,0.001839014,0.196302],"study_design_scores_gemma":[0.00002114881,0.0005651197,0.5711586,0.00004888225,0.0002432856,0.0001349256,0.001888505,0.4007154,0.01478295,0.007478875,0.002886722,0.00007548473],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9080173,0.0002858172,0.08676755,0.0002970852,0.0000344842,0.0001677302,0.0007542979,0.0002782157,0.003397505],"genre_scores_gemma":[0.98037,0.0001637279,0.01863903,0.0000389099,0.00001387924,0.00004996992,0.0003144966,0.00001735417,0.0003925687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01230229,"threshold_uncertainty_score":0.02446133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07422810352594321,"score_gpt":0.3112244453648216,"score_spread":0.2369963418388784,"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."}}