{"id":"W3153037669","doi":"10.1155/2021/5521526","title":"Forecasting Civil Aviation Incident Rate in China Using a Combined Prediction Model","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Graduate Education; Nanjing University; Nanjing University of Posts and Telecommunications; Government of Jiangsu Province","keywords":"Civil aviation; Exponential smoothing; Aviation; Aviation safety; Operator (biology); Statistics; Operations research; Engineering; Mathematics; Aerospace engineering","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":[0.0003680251,0.00009910616,0.0001963082,0.0002337809,0.00006639618,0.00001867796,0.00004817429,0.00007074061,0.0003098203],"category_scores_gemma":[0.00005981199,0.0001054934,0.00009939195,0.0002660698,0.00001067483,0.0006929802,0.000001353799,0.000259532,0.000002983989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001630386,"about_ca_system_score_gemma":0.0000957288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001226391,"about_ca_topic_score_gemma":0.0003877852,"domain_scores_codex":[0.998512,0.0001068481,0.0009095466,0.0001362116,0.0002071317,0.0001283042],"domain_scores_gemma":[0.9987837,0.00004945075,0.0007056328,0.00008677046,0.0003226294,0.00005180655],"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":[0.000475218,0.000216229,0.01009535,0.00002588545,0.00004380251,0.0001052856,0.01409806,0.9547079,0.01537657,0.001681357,0.00003908634,0.003135326],"study_design_scores_gemma":[0.003362749,0.0001255017,0.7933154,0.000209884,0.00005231172,0.00006572561,0.002037697,0.1958034,0.001100123,0.00373817,0.00006740723,0.0001216891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8901234,0.00004302193,0.1080947,0.0001630157,0.001039135,0.00009511381,0.00001114229,0.00001771458,0.0004128283],"genre_scores_gemma":[0.9956121,0.00002112074,0.003974691,0.00007386854,0.00008555148,0.000005481525,0.00006250286,0.00001486614,0.0001497456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7832201,"threshold_uncertainty_score":0.4301898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03175591110116262,"score_gpt":0.3322496779578128,"score_spread":0.3004937668566502,"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."}}