{"id":"W570623705","doi":"","title":"STUDY HIGHLIGHTS THE POTENTIAL BENEFIT OF IMPROVED ACCURACY IN AERODROME FORECASTS.","year":2001,"lang":"en","type":"article","venue":"ICAO bulletin","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Meteorology; Environmental science; Subtitle; Value (mathematics); Computer science; Statistics; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001634058,0.0002828575,0.0003634734,0.0005878169,0.0008209508,0.001389699,0.000810599,0.0007285803,0.005392291],"category_scores_gemma":[0.01483748,0.0001240725,0.0003425241,0.001191115,0.0004246745,0.001147401,0.0005483684,0.0008108484,0.001008264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003322055,"about_ca_system_score_gemma":0.004077594,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6032431,"about_ca_topic_score_gemma":0.6627254,"domain_scores_codex":[0.9984334,0.0002955826,0.00005200711,0.0001448791,0.000880321,0.0001938556],"domain_scores_gemma":[0.9918802,0.002698263,0.000518568,0.000571302,0.004138083,0.0001935056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002278163,0.0006170184,0.3610924,0.0006275951,0.000550698,0.001306587,0.001140653,0.1409574,0.01448073,0.01412687,0.07787494,0.3849469],"study_design_scores_gemma":[0.0001848192,0.0007171659,0.6580983,0.0001786605,0.000342167,0.001064077,0.003093143,0.1960363,0.0284892,0.008417348,0.1031989,0.0001799778],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7570539,0.002075763,0.0397094,0.03334875,0.0007914701,0.0002903971,0.01374382,0.002241865,0.1507446],"genre_scores_gemma":[0.9875535,0.0002381376,0.005523846,0.0005230153,0.0000826201,0.00001486567,0.001136973,0.0000307619,0.0048963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6032431,"threshold_uncertainty_score":0.798187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007391162191865888,"score_gpt":0.1986189070744558,"score_spread":0.1912277448825899,"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."}}