{"id":"W1665137144","doi":"10.3968/j.css.1923669720130906.2976","title":"Predication Method for China's Civil Aviation Fuel Consumption","year":2013,"lang":"en","type":"article","venue":"Canadian social science","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Civil aviation; China; Aviation; Fuel efficiency; Artificial neural network; Transportation industry; Gray (unit); Aviation engineering; Consumption (sociology); Network model; System dynamics; Transport engineering; Aeronautics; Computer science; Operations research; Engineering; Automotive engineering; Artificial intelligence; Aerospace engineering; Geography","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":[0.0004217793,0.0004299333,0.0003245741,0.001786261,0.0002336739,0.0005773522,0.0003869057,0.0003367198,0.001389518],"category_scores_gemma":[0.00181739,0.0001553638,0.0005037496,0.001065318,0.0001686593,0.0005442742,0.0002904584,0.0002952846,0.0002091125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009326884,"about_ca_system_score_gemma":0.001045131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04408981,"about_ca_topic_score_gemma":0.02384018,"domain_scores_codex":[0.9998178,0.00003049306,0.00001301977,0.00005928289,0.00005457574,0.00002486882],"domain_scores_gemma":[0.999681,0.0001263527,0.00003010094,0.00001976392,0.0001253324,0.00001742822],"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":[0.0001164894,0.00007564211,0.06907827,0.00007244978,0.0001017922,0.0002040184,0.0001561622,0.7907535,0.002520011,0.002610253,0.002449679,0.1318617],"study_design_scores_gemma":[0.000002169428,0.000005985713,0.004944,0.000002287945,0.000008097696,0.000006399564,0.00001530512,0.9940112,0.0003854967,0.0004674665,0.0001475589,0.000004011136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7424305,0.00023152,0.2512791,0.0002531158,0.00004293453,0.0000600442,0.0007244139,0.001095628,0.003882741],"genre_scores_gemma":[0.9885957,0.00007782297,0.009847666,0.00001446487,0.000005733226,0.00002480558,0.00041837,0.00001770111,0.0009977316],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04408981,"threshold_uncertainty_score":0.08766633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06738840093481795,"score_gpt":0.3953794307401045,"score_spread":0.3279910298052865,"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."}}