{"id":"W3136265257","doi":"10.1155/2021/6638130","title":"A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Artificial neural network; Graph; Robustness (evolution); Artificial intelligence; Deep learning; Kernel (algebra); Test set; Algorithm; Data mining; Theoretical computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001414443,0.0001174697,0.0001921734,0.00009873953,0.00005036475,0.00001631101,0.00006946933,0.00006637323,0.000004786365],"category_scores_gemma":[0.000007026833,0.0001209912,0.000169159,0.0002336566,0.00001222867,0.0004350222,0.000001016919,0.000159074,1.644514e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000342032,"about_ca_system_score_gemma":0.00001505898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.920581e-7,"about_ca_topic_score_gemma":0.000009688017,"domain_scores_codex":[0.9990444,0.00001140247,0.0004888152,0.0001085652,0.0001846389,0.0001621307],"domain_scores_gemma":[0.9995036,0.00001765861,0.0001409008,0.00009148705,0.0001796122,0.00006675933],"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":[0.0000563797,0.00003967387,0.0002437081,0.00008412565,0.00008855361,0.00001790795,0.0002742749,0.9742491,0.001081149,0.0003663345,0.002363502,0.02113527],"study_design_scores_gemma":[0.005253658,0.0006617239,0.08752161,0.000201678,0.0007648316,0.0001837298,0.002005622,0.87376,0.005149204,0.002381003,0.02148426,0.0006326534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03605995,0.0006782628,0.961296,0.00002691213,0.0008752119,0.0002176721,0.00001180623,0.000582558,0.0002516907],"genre_scores_gemma":[0.8690583,0.0005851827,0.1298291,0.00004604938,0.0002422932,0.00003127086,0.000170609,0.00002515673,0.00001207805],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8329983,"threshold_uncertainty_score":0.4933878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007205169992439184,"score_gpt":0.2165088119396194,"score_spread":0.2093036419471802,"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."}}