{"id":"W2141125834","doi":"10.1002/atr.5670340207","title":"Optimal scheduling of public transport fleet at network level","year":2000,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scheduling (production processes); Public transport; Operator (biology); TRIPS architecture; Computer science; Operations research; Mathematical optimization; Dynamic programming; Routing (electronic design automation); Service (business); Vehicle routing problem; Fleet management; Transport engineering; Engineering; Computer network; Telecommunications; Mathematics; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0006149192,0.0005327534,0.0006531993,0.0004368011,0.0005295658,0.001071801,0.0006978572,0.0005870702,0.004338174],"category_scores_gemma":[0.00119638,0.0003697636,0.0003063222,0.0005442197,0.0005376442,0.0006861378,0.0004602754,0.0004825201,0.0003360608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002619001,"about_ca_system_score_gemma":0.002235518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02191691,"about_ca_topic_score_gemma":0.01486099,"domain_scores_codex":[0.9996384,0.000120162,0.000008501228,0.00005201925,0.00004366838,0.0001373217],"domain_scores_gemma":[0.9995576,0.0001775305,0.00007122025,0.00002717326,0.00007509649,0.00009137844],"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":[0.00007982868,0.00002425034,0.0002228232,0.0000171018,0.000007519875,0.00003009985,0.00001892535,0.9889053,0.0009126443,0.004620469,0.0004432016,0.004717809],"study_design_scores_gemma":[0.000008802203,0.0000254748,0.0001262156,0.000001735734,0.00000321216,0.000003593138,0.00002157956,0.9973002,0.0002970031,0.001876699,0.0003330884,0.000002374426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4706619,0.0002729816,0.4972707,0.0005373415,0.00008619099,0.0001985492,0.0003192883,0.0005899316,0.03006313],"genre_scores_gemma":[0.9744138,0.00006518755,0.02184548,0.00001658177,0.00001305185,0.00004628863,0.00009474,0.00004203733,0.003462913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02191691,"threshold_uncertainty_score":0.04357868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02261428677898578,"score_gpt":0.2456040060273018,"score_spread":0.222989719248316,"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."}}