{"id":"W2558921574","doi":"10.1007/s10109-016-0244-8","title":"Discovering the space–time dimensions of schedule padding and delay from GTFS and real-time transit data","year":2016,"lang":"en","type":"article","venue":"Journal of Geographical Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Padding; Schedule; Transit (satellite); Prepayment of loan; Computer science; Transport engineering; Operations research; Computer security; Business; Public transport; Engineering; Finance","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001019398,0.00006706712,0.0002075821,0.00007459744,0.0002285888,0.00006444523,0.0001731558,0.00007330855,0.00001828743],"category_scores_gemma":[0.0001096845,0.0000330711,0.00004236346,0.0001777729,0.0002285324,0.000360784,0.00001601103,0.00008154748,8.955488e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007093767,"about_ca_system_score_gemma":0.00003830761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001680597,"about_ca_topic_score_gemma":0.0001626198,"domain_scores_codex":[0.9989096,0.0001782765,0.0003469937,0.0001188741,0.0003242555,0.0001219788],"domain_scores_gemma":[0.9988475,0.0005434066,0.0002712751,0.0001342366,0.00008875991,0.0001148034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005217341,0.0002420557,0.8608807,0.0001066448,0.001068742,0.00007338153,0.02168131,0.003847962,0.08983172,0.01348674,0.002289912,0.005969095],"study_design_scores_gemma":[0.008224226,0.0009719548,0.8807836,0.008425244,0.001956562,0.0001727799,0.02115349,0.02599763,0.0004548659,0.002958626,0.04741986,0.001481202],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930078,0.0008113877,0.003726643,0.001942247,0.0001628453,0.00009559812,0.00009880048,0.00001179767,0.000142822],"genre_scores_gemma":[0.9978511,0.001331711,0.0006118845,0.000006600439,0.0001275625,5.591995e-7,0.000005788964,0.000005605334,0.00005921365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08937685,"threshold_uncertainty_score":0.2540571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712484488992029,"score_gpt":0.2614670763010025,"score_spread":0.2443422314110822,"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."}}