{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01496787,0.0004534919,0.0006236244,0.003312085,0.002583252,0.0002930211,0.00108352,0.0005743248,0.0009697096],"category_scores_gemma":[0.001735294,0.0004203231,0.0002905813,0.005738843,0.001717084,0.002079709,0.00001307313,0.001574506,0.0002929996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001178737,"about_ca_system_score_gemma":0.001407771,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03797565,"about_ca_topic_score_gemma":0.3070794,"domain_scores_codex":[0.9851223,0.00315222,0.001685038,0.001461829,0.005363672,0.003214992],"domain_scores_gemma":[0.9911682,0.002671064,0.0002988594,0.0006685759,0.004083774,0.001109508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009502328,0.0006126871,0.6004354,0.0002134071,0.00008379841,0.0003184386,0.06685758,0.0005698075,0.001611824,0.3176477,0.008139798,0.002559377],"study_design_scores_gemma":[0.003376938,0.0002868575,0.8212568,0.0008391878,0.00003471372,2.942928e-7,0.104479,0.0000611012,0.0003938582,0.01026783,0.05826452,0.0007389833],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9501625,0.0003691139,0.01791571,0.01434034,0.0006518112,0.002805728,0.001178434,0.0007298538,0.01184656],"genre_scores_gemma":[0.9886512,0.001716807,0.002717199,0.0001120113,0.00032083,0.0005085747,0.0004424011,0.0001167304,0.005414264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3073798,"threshold_uncertainty_score":0.9999436,"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."}}