{"id":"W2142232457","doi":"10.1007/s10479-010-0710-5","title":"Designing the master schedule for demand-adaptive transit systems","year":2010,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; HEC Montréal","funders":"Regione Lombardia; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Reservation; Computer science; Schedule; Operations research; Line (geometry); Theory of computation; Bus rapid transit; Set (abstract data type); Process (computing); Focus (optics); Real-time computing; Transport engineering; Public transport; Computer network; Engineering; Algorithm; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001122275,0.00006075151,0.00008605974,0.0001401565,0.0002747604,0.0000752496,0.0001551849,0.00005816011,0.00005095958],"category_scores_gemma":[0.00007055358,0.00004696424,0.0000416267,0.0003513429,0.00008802216,0.000166769,0.000004180626,0.0002814501,0.00001335305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005602642,"about_ca_system_score_gemma":0.0000711075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006567329,"about_ca_topic_score_gemma":0.0005004606,"domain_scores_codex":[0.9992239,0.00004193816,0.0002347137,0.0000950902,0.0002047772,0.0001995797],"domain_scores_gemma":[0.9986755,0.0001736683,0.00000626824,0.0002148208,0.0008900178,0.00003971923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000237826,0.0000740891,0.0001328175,0.00009762545,0.0001079436,5.124043e-7,0.002180263,0.5591521,0.3036907,0.1280589,0.005303515,0.001177815],"study_design_scores_gemma":[0.0007028358,0.0002363787,0.007913115,0.00007122366,0.00002242234,0.000004642466,0.004287,0.6936518,0.2710181,0.000494506,0.02130262,0.000295392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5353964,0.0001833632,0.457017,0.003257985,0.0002498749,0.001413299,0.0001231847,0.00007816955,0.002280688],"genre_scores_gemma":[0.9953536,0.00001841791,0.003749639,0.000031936,0.00004958935,0.0003838718,0.00002594464,0.00001434945,0.0003725971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4599572,"threshold_uncertainty_score":0.2113263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2757419418382559,"score_gpt":0.4107279384061709,"score_spread":0.134985996567915,"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."}}