{"id":"W2912382216","doi":"10.1175/amsmonographs-d-18-0020.1","title":"100 Years of Progress in Forecasting and NWP Applications","year":2019,"lang":"en","type":"article","venue":"Meteorological Monographs","topic":"Climate variability and models","field":"Environmental Science","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"National Oceanic and Atmospheric Administration; Met Office; Northwest Airlines; Environment and Climate Change Canada; National Aeronautics and Space Administration","keywords":"Meteorology; Weather forecasting; Government (linguistics); Climatology; Global Forecast System; Numerical weather prediction; Earth system science; Environmental science; Computer science; Geography; Geology; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003081699,0.00005724882,0.0001292518,0.00003675036,0.00001699599,0.000006573155,0.0001041387,0.00006577554,0.0003108418],"category_scores_gemma":[0.00001494812,0.00004912185,0.00003422971,0.0002709034,0.0002222325,0.00006095909,0.0001332314,0.00007633529,0.00002128301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000972608,"about_ca_system_score_gemma":0.000001248797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003491649,"about_ca_topic_score_gemma":0.00001616636,"domain_scores_codex":[0.9993407,0.00003884348,0.0001608735,0.0002186962,0.00008738512,0.0001535094],"domain_scores_gemma":[0.9996589,0.00011053,0.00004442438,0.0001433291,0.00000259479,0.00004022222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001492358,0.00008775049,0.9629543,0.000008592197,0.000002306567,6.823062e-7,0.0001235787,0.0001908103,0.000512737,0.0006445337,0.00000404481,0.03545571],"study_design_scores_gemma":[0.0003158221,0.0001957021,0.9755763,0.00000671096,0.000007061297,0.000002689239,0.00006481703,0.009156609,0.00009056506,0.01310958,0.001363993,0.0001102001],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972065,0.00008316007,0.000139818,0.00005142809,0.00001069784,0.0003530117,0.000004346442,0.0000156105,0.002135399],"genre_scores_gemma":[0.9952222,0.00002071386,0.004626207,0.00004480356,0.000002633622,0.00006966655,0.000002491,0.000003096848,0.000008260412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03534551,"threshold_uncertainty_score":0.34035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02802673492687282,"score_gpt":0.2490172035156447,"score_spread":0.2209904685887719,"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."}}