{"id":"W3024736694","doi":"10.5194/essd-12-2381-2020","title":"SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018","year":2020,"lang":"en","type":"article","venue":"Earth system science data","topic":"Climate variability and models","field":"Environmental Science","cited_by":79,"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; Canada First Research Excellence Fund","keywords":"Precipitation; Environmental science; Hydrometeorology; Climatology; Meteorology; Quantile; Benchmark (surveying); Interpolation (computer graphics); Computer science; Statistics; Geography; Mathematics; Geology; Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0006270658,0.0005334749,0.0005806228,0.001489905,0.0004499693,0.0005433948,0.00125144,0.0004537695,0.003315724],"category_scores_gemma":[0.002150345,0.000264585,0.0004566864,0.00267646,0.0002224431,0.0005899745,0.0008203189,0.0008733892,0.002581033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008218086,"about_ca_system_score_gemma":0.002454613,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09556939,"about_ca_topic_score_gemma":0.1313079,"domain_scores_codex":[0.9995165,0.00007493946,0.00006024932,0.0001730274,0.0001275729,0.00004763896],"domain_scores_gemma":[0.9983237,0.0001006775,0.0002859526,0.0003524404,0.0008062412,0.0001308193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005274648,0.0003325338,0.1987424,0.0005970686,0.0005615973,0.0003358026,0.0004191556,0.0161128,0.003585289,0.00202252,0.7279292,0.04883409],"study_design_scores_gemma":[0.000401919,0.00007439386,0.5018288,0.0001874121,0.000115182,0.0001346789,0.0004516713,0.02083789,0.003129529,0.001340097,0.4714047,0.00009367922],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05900118,0.0002125239,0.002306835,0.0002603596,0.0001142444,0.00009403477,0.9347633,0.0009131482,0.002334375],"genre_scores_gemma":[0.05012707,0.00008555532,0.006315012,0.00006267113,0.00005619283,0.0003106225,0.9420013,0.00009351697,0.0009480336],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9044306,"threshold_uncertainty_score":0.1900262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06464931966081024,"score_gpt":0.2689983524942278,"score_spread":0.2043490328334175,"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."}}