{"id":"W2345612734","doi":"","title":"Identifying TDM Corridors in Large Metropolitan Regions","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Transport engineering; Transit (satellite); Demand management; Service (business); Business; Computer science; Public transport; Geography; Engineering; Economics; Marketing","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":[0.0008748486,0.0005265021,0.0003091682,0.003157431,0.0006260722,0.001578838,0.0006450127,0.0005330503,0.001603904],"category_scores_gemma":[0.004155883,0.0003553153,0.0005346157,0.003420595,0.000470631,0.0009359136,0.001299579,0.0004263609,0.0001650164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002102142,"about_ca_system_score_gemma":0.00187467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08378349,"about_ca_topic_score_gemma":0.1200653,"domain_scores_codex":[0.9993274,0.0003122018,0.00003013627,0.0001027484,0.0001213648,0.0001060779],"domain_scores_gemma":[0.9976661,0.000946745,0.0006360123,0.0001233642,0.0004148775,0.0002129046],"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":[0.0004308325,0.0002106199,0.3512415,0.0003590218,0.0001887854,0.0005640874,0.001185368,0.5474038,0.003815071,0.01776952,0.002528474,0.07430281],"study_design_scores_gemma":[0.00003877846,0.0001635231,0.17646,0.0001197294,0.00005922565,0.0001906927,0.004720683,0.7997634,0.002073593,0.009025555,0.007308567,0.00007628396],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9265679,0.0003731896,0.06386274,0.0002741023,0.00001119841,0.0001828503,0.0033842,0.0002984054,0.005045532],"genre_scores_gemma":[0.9631736,0.0001544962,0.03409997,0.00001483353,0.00000408945,0.0001038897,0.001418609,0.00002000205,0.001010409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08378349,"threshold_uncertainty_score":0.1665916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09206767954289677,"score_gpt":0.4279862255438653,"score_spread":0.3359185460009685,"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."}}