{"id":"W3044306824","doi":"10.1155/2020/8365194","title":"Designing High-Freedom Responsive Feeder Transit System with Multitype Vehicles","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Changsha University of Science and Technology; National Natural Science Foundation of China; Natural Science Foundation of Hunan Province; Education Department of Hunan Province","keywords":"Reservation; Scheduling (production processes); Computer science; Public transport; Mathematical optimization; Last mile (transportation); Metaheuristic; Mode (computer interface); Integer programming; Heuristic; Service level; Service (business); Transport engineering; Engineering; Simulation; Mile; Algorithm; Computer network; Mathematics","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":[0.0002977086,0.0007101843,0.0004917383,0.0003918989,0.0006581991,0.0007910082,0.0008783073,0.0006380694,0.002266331],"category_scores_gemma":[0.0003707975,0.0002893818,0.0007559936,0.0004525647,0.0002691592,0.0006431754,0.0005799339,0.0004056314,0.0002762222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007965512,"about_ca_system_score_gemma":0.001070762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007413554,"about_ca_topic_score_gemma":0.007584901,"domain_scores_codex":[0.9996849,0.00008573243,0.00001121577,0.0000665531,0.00006188387,0.00008970059],"domain_scores_gemma":[0.9998472,0.00003605932,0.00003898548,0.00001224715,0.0000345795,0.00003092016],"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":[0.0001052669,0.00006814428,0.001299139,0.0001310052,0.00003974976,0.0002533845,0.00008196137,0.9539967,0.01457681,0.003995234,0.0005555187,0.02489709],"study_design_scores_gemma":[0.00001287949,0.0001684394,0.0003070224,0.000005119824,0.00002059833,0.0000564507,0.00006641908,0.9960214,0.001427728,0.0007382471,0.001167273,0.000008263461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2261724,0.0003034323,0.7594641,0.0001914483,0.00005452459,0.0001776165,0.00011342,0.0004852647,0.01303776],"genre_scores_gemma":[0.9724501,0.0001197878,0.02443004,0.00002084305,0.00001283645,0.00007331454,0.00007140697,0.00001815013,0.002803572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007413554,"threshold_uncertainty_score":0.01474082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01066881693463832,"score_gpt":0.2088015652497782,"score_spread":0.1981327483151398,"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."}}