{"id":"W4399541821","doi":"10.1175/mwr-d-23-0273.1","title":"Leveraging Deterministic Weather Forecasts for In Situ Probabilistic Temperature Predictions via Deep Learning","year":2024,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Grand Équipement National De Calcul Intensif; Environment and Climate Change Canada; Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Probabilistic logic; Probabilistic forecasting; Computer science; Lead time; Calibration; Forecast skill; Numerical weather prediction; Artificial neural network; Ensemble forecasting; Consensus forecast; Weather forecasting; Probability distribution; Forecast verification; Meteorology; Machine learning; Artificial intelligence; Econometrics; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005188294,0.0001926152,0.0003287425,0.00008022545,0.0001682543,0.00008998421,0.0001464275,0.00007807189,0.001294032],"category_scores_gemma":[0.0002122598,0.0001355088,0.0001453183,0.0003364391,0.00003980294,0.0001407229,0.000009456567,0.0002696676,0.0001399258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001603342,"about_ca_system_score_gemma":0.00002869929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004594257,"about_ca_topic_score_gemma":0.0004641319,"domain_scores_codex":[0.9986187,0.0001502298,0.0003703003,0.0003915596,0.00014689,0.0003223232],"domain_scores_gemma":[0.9992107,0.0004455479,0.00004445494,0.0001706646,0.00002952493,0.00009916331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000521253,0.0001059389,0.02757774,0.0064623,0.0001207097,0.0001123869,0.001882416,0.1350576,0.0001686987,0.000537449,0.0006776694,0.827245],"study_design_scores_gemma":[0.0004320676,0.0005420466,0.0492566,0.003989374,0.000287422,0.00002643947,0.00006754194,0.7366127,0.000003536327,0.01481151,0.1933397,0.0006310715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2248786,0.7196469,0.00640372,0.002094703,0.001234192,0.005570917,0.0001649408,0.0006031943,0.0394029],"genre_scores_gemma":[0.9961522,0.001757634,0.0005680803,0.000390859,0.0001266783,0.00007613755,0.0001424887,0.00001231297,0.0007736494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8266139,"threshold_uncertainty_score":0.9996189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02819614565013163,"score_gpt":0.2548368210188298,"score_spread":0.2266406753686982,"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."}}