{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001515649,0.000128972,0.0002051869,0.00002522001,0.000496393,0.0001820935,0.001812486,0.00004041598,0.0005683294],"category_scores_gemma":[0.0003081237,0.0001065641,0.00002536595,0.0006599033,0.0006185226,0.001510863,0.001767601,0.00007904045,0.0005201187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005295571,"about_ca_system_score_gemma":0.00009612895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002775027,"about_ca_topic_score_gemma":0.001397572,"domain_scores_codex":[0.9974227,0.00008978863,0.0002688451,0.001181696,0.0005316451,0.0005053253],"domain_scores_gemma":[0.9970492,0.0001011166,0.00008293542,0.002475924,0.00002454612,0.000266262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004347223,0.003943524,0.1830063,0.0008590913,0.0001165709,0.0006271647,0.003923412,0.05102083,0.3417797,0.0089313,0.2567416,0.1486157],"study_design_scores_gemma":[0.0006111301,0.0003436404,0.02796271,0.00002725579,0.00005693226,0.0001411383,0.001303515,0.3950945,0.001863788,0.00019718,0.571737,0.0006612188],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.915529,0.00003119713,0.02326302,0.0004794512,0.000516021,0.0007168265,0.05711241,0.0001209423,0.00223116],"genre_scores_gemma":[0.9553704,0.000006906719,0.02360941,0.0004739091,0.00006063247,0.00002670463,0.02037471,0.000007956197,0.00006939047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3440737,"threshold_uncertainty_score":0.6685247,"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."}}