{"id":"W3111205597","doi":"10.5194/essd-13-3337-2021","title":"EMDNA: an Ensemble Meteorological Dataset for North America","year":2021,"lang":"en","type":"article","venue":"Earth system science data","topic":"Climate variability and models","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Canmore Museum and Geoscience Centre; University of Saskatchewan","funders":"Global Water Futures","keywords":"Probabilistic logic; Precipitation; Environmental science; Range (aeronautics); Ensemble forecasting; Meteorology; Computer science; Climatology; Statistics; Mathematics; Geography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008909689,0.0007152389,0.0006505564,0.001210662,0.0004565378,0.0005473848,0.001577322,0.0005600775,0.00310183],"category_scores_gemma":[0.002398065,0.0002990855,0.0007981007,0.00330668,0.0001885513,0.0008743565,0.0009721391,0.0009358271,0.001894673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008705826,"about_ca_system_score_gemma":0.00185232,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09883638,"about_ca_topic_score_gemma":0.1332228,"domain_scores_codex":[0.9993466,0.0001525104,0.00008088531,0.0002019137,0.0001657984,0.00005224795],"domain_scores_gemma":[0.9983216,0.0002114408,0.0001899427,0.0004061501,0.0007414131,0.0001294082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002606745,0.0002311868,0.09822857,0.0006790616,0.0009102444,0.0003439834,0.0003918631,0.05341448,0.003183194,0.00205235,0.7815987,0.0587056],"study_design_scores_gemma":[0.0005316176,0.00007874377,0.2980053,0.0002812138,0.000217566,0.000168135,0.0006450628,0.1790523,0.004395843,0.00427439,0.5121421,0.0002077594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03911529,0.0003439045,0.006029736,0.000360865,0.0001685296,0.000117781,0.9487669,0.003316524,0.0017805],"genre_scores_gemma":[0.04688651,0.0001507735,0.01070559,0.00009065941,0.00005419766,0.0003416804,0.9410418,0.000162074,0.0005666929],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9011636,"threshold_uncertainty_score":0.1965222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08433598375270702,"score_gpt":0.3056725776002202,"score_spread":0.2213365938475132,"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."}}