{"id":"W2948463670","doi":"10.1155/2019/3525912","title":"Study of Flight Departure Delay and Causal Factor Using Spatial Analysis","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Aviation Industry Analysis and Trends","field":"Economics, Econometrics and Finance","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Lag; Variable (mathematics); Spatial correlation; Computer science; Statistics; Ordinary least squares; Econometrics; Regression analysis; Causal analysis; Correlation; Regression; Mathematics; Control theory (sociology); Control (management); Artificial intelligence","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":[0.002119802,0.0005181772,0.000430676,0.003002266,0.0004043502,0.001013317,0.0005983806,0.0004765774,0.002634464],"category_scores_gemma":[0.009633175,0.0002318986,0.001195192,0.003601908,0.0005481053,0.001546261,0.00104806,0.0005732966,0.0002046214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009513475,"about_ca_system_score_gemma":0.001390328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02335025,"about_ca_topic_score_gemma":0.009799073,"domain_scores_codex":[0.9987603,0.0004839253,0.00008194825,0.0002992915,0.0002477974,0.0001268038],"domain_scores_gemma":[0.9918082,0.005176445,0.001396536,0.0004517506,0.0009966755,0.0001702815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002090568,0.0001449272,0.6167028,0.0002722734,0.0006788127,0.0008921259,0.001139263,0.2007371,0.002609143,0.08346401,0.001077864,0.09207261],"study_design_scores_gemma":[0.00001727275,0.0001536932,0.1527295,0.00006524643,0.0003236243,0.0003000296,0.001498312,0.8134064,0.001545944,0.02601576,0.003877908,0.00006629502],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6981628,0.0007912046,0.2947135,0.0003398529,0.00004314497,0.00009295772,0.0007535631,0.0001425885,0.0049605],"genre_scores_gemma":[0.9866641,0.0002937664,0.01173237,0.00001141148,0.00001849594,0.00003104095,0.0002813273,0.00001295241,0.0009546126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02335025,"threshold_uncertainty_score":0.04642862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02504388372617479,"score_gpt":0.2568757355382747,"score_spread":0.2318318518120999,"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."}}