{"id":"W2988776809","doi":"10.1175/bams-d-19-0143.1","title":"The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally","year":2019,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Esri (Canada); Environment and Climate Change Canada; McMaster University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; Compute Canada","keywords":"Numerical weather prediction; Computer science; Database; NetCDF; Meteorology; Benchmark (surveying); Upload; Shapefile; Weather forecasting; Environmental science; Metadata; Data mining; Geography; 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":[0.002453877,0.001347676,0.0006819987,0.00416297,0.002741376,0.003854432,0.003378778,0.00067168,0.02911224],"category_scores_gemma":[0.007330269,0.0005618131,0.0009461566,0.007413025,0.000786013,0.002706866,0.003573275,0.001742716,0.01106139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01344208,"about_ca_system_score_gemma":0.04496498,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9693164,"about_ca_topic_score_gemma":0.9614655,"domain_scores_codex":[0.9982775,0.0001266538,0.00007045407,0.0001725862,0.001142353,0.0002104869],"domain_scores_gemma":[0.9905502,0.000411448,0.0002262359,0.001293205,0.006617947,0.000900923],"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":[0.0001689783,0.00008433947,0.01020392,0.0001964101,0.0001129525,0.0001448284,0.0003587842,0.04577642,0.001816076,0.01122147,0.8418889,0.08802692],"study_design_scores_gemma":[0.0001973139,0.00002789408,0.01840261,0.0002923273,0.0000868658,0.00005914825,0.0006149328,0.1917012,0.003946309,0.007657295,0.7766933,0.0003207897],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.03010524,0.00118791,0.1050954,0.00420458,0.000651322,0.0005359709,0.6514468,0.1191627,0.08761014],"genre_scores_gemma":[0.1451404,0.002120963,0.1644018,0.0004896107,0.0001446584,0.0004938392,0.6505888,0.01759203,0.01902792],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03068364,"threshold_uncertainty_score":0.09752953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005726692297731721,"score_gpt":0.1834999125298671,"score_spread":0.1777732202321354,"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."}}