{"id":"W4396974604","doi":"10.1155/2024/4349402","title":"Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Distillation; Computer science; Traffic flow (computer networking); Flow network; Flow (mathematics); Transport engineering; Environmental science; Engineering; Computer network; Chemistry; Mechanics; Mathematics; Chromatography; Mathematical optimization","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":[0.0004659129,0.0007844824,0.0007996522,0.0007418137,0.0004222693,0.0007427867,0.001247203,0.0007987745,0.0008042472],"category_scores_gemma":[0.001914196,0.0004957133,0.0006565471,0.001016758,0.0004832425,0.001539327,0.0009677836,0.001162543,0.0001477004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188832,"about_ca_system_score_gemma":0.001343003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03655898,"about_ca_topic_score_gemma":0.02381304,"domain_scores_codex":[0.9996953,0.00005113418,0.00002081842,0.0001280301,0.00006152238,0.00004325108],"domain_scores_gemma":[0.9993706,0.000321776,0.00008389907,0.00006000842,0.0001282145,0.00003552724],"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":[0.00004682411,0.00002725921,0.001037553,0.00001811769,0.00001765312,0.00003145011,0.00001824447,0.9784734,0.0004696689,0.001391265,0.0003306655,0.01813786],"study_design_scores_gemma":[9.187687e-7,0.000002043231,0.00005080455,5.981428e-7,0.000001372261,0.000001294459,9.990683e-7,0.9994004,0.00006583789,0.0004315136,0.00004331484,9.617352e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1630195,0.0005254422,0.831583,0.000569879,0.00007198843,0.00006319149,0.001142372,0.0009933906,0.002031257],"genre_scores_gemma":[0.940649,0.0002298421,0.05617836,0.0001109398,0.00003561703,0.00009876546,0.001467762,0.00002738201,0.001202262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03655898,"threshold_uncertainty_score":0.07269239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004538420360686683,"score_gpt":0.2086804372877968,"score_spread":0.2041420169271101,"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."}}