{"id":"W1782572543","doi":"10.1002/atr.176","title":"The use of demand correlation in the modeling of air carrier departure delays as first‐order autoregressive random processes","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive model; Estimator; A priori and a posteriori; Autocorrelation; STAR model; Statistics; White noise; Econometrics; Mathematics; Computer science; Applied mathematics; Autoregressive integrated moving average; Time series","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":[0.001239458,0.00041732,0.0003628326,0.0003402334,0.0001916943,0.0006689446,0.0005060614,0.0004383248,0.0005224327],"category_scores_gemma":[0.002524285,0.0003947692,0.0005704693,0.0003858246,0.0003080002,0.0005626724,0.000298283,0.0009304251,0.0002153815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00053797,"about_ca_system_score_gemma":0.001288606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009350831,"about_ca_topic_score_gemma":0.008116985,"domain_scores_codex":[0.9995903,0.0001756408,0.00001739235,0.00007206351,0.0001084321,0.00003609712],"domain_scores_gemma":[0.9988911,0.000652241,0.0001799935,0.0001085304,0.0001445373,0.000023619],"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":[0.00002413217,0.0000229908,0.001304396,0.00001323272,0.00003022934,0.00004982873,0.00002602738,0.975994,0.00202086,0.00749058,0.0003192918,0.01270438],"study_design_scores_gemma":[0.00000170468,0.000008468797,0.0002755493,0.000001370482,0.000003928795,0.000007867296,0.000001645645,0.9981288,0.0004094047,0.0009242849,0.0002321425,0.000004864516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06425928,0.0001253469,0.9339936,0.0001740535,0.00003518814,0.000020653,0.00007963265,0.0002029159,0.001109397],"genre_scores_gemma":[0.8988808,0.0002705719,0.09853898,0.0000493022,0.00005998627,0.00006527377,0.0001605233,0.00005785693,0.001916759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009350831,"threshold_uncertainty_score":0.01859283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0203379944291965,"score_gpt":0.2329611572902198,"score_spread":0.2126231628610233,"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."}}