{"id":"W3208175508","doi":"10.1155/2021/9231451","title":"Mining Travel Time of Airport Ferry Network Based on Historical Trajectory Data","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Schedule; Transport engineering; Travel time; Shortest path problem; Computer science; Global Positioning System; Operations research; Scheduling (production processes); Terminal (telecommunication); Graph; Real-time computing; Engineering; Computer network; Telecommunications; Operations management","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":[0.0001365983,0.0005311156,0.0002466342,0.003956546,0.0003257577,0.0004276496,0.0005073301,0.0003179956,0.0008301558],"category_scores_gemma":[0.0008491632,0.0001522733,0.0004987676,0.003507268,0.0001295242,0.0007913811,0.0003035456,0.0002425289,0.0003276514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005794454,"about_ca_system_score_gemma":0.0006118349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03395669,"about_ca_topic_score_gemma":0.04018123,"domain_scores_codex":[0.9998137,0.00001445043,0.00001924751,0.00006966118,0.00005115165,0.00003186114],"domain_scores_gemma":[0.9997213,0.00004584816,0.00006262321,0.00003976639,0.0001022996,0.0000282312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004545349,0.0002251857,0.2215224,0.0006621326,0.00034989,0.001797605,0.0006441729,0.4408526,0.02514264,0.005614743,0.01320967,0.2895244],"study_design_scores_gemma":[0.00001775469,0.0001055887,0.1115975,0.00005039462,0.0001150024,0.0004530012,0.001047556,0.864119,0.009019218,0.003129067,0.01029588,0.00004995001],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8417698,0.0006689667,0.1304076,0.0002171717,0.0000813586,0.0001020546,0.02218972,0.0012456,0.003317692],"genre_scores_gemma":[0.9465151,0.0004448243,0.03370724,0.00001016628,0.00001265849,0.00006206449,0.01793022,0.00004598951,0.001271698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03395669,"threshold_uncertainty_score":0.06751806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01603090701360471,"score_gpt":0.2357570320140029,"score_spread":0.2197261250003981,"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."}}