{"id":"W1124213845","doi":"10.1038/sdata.2015.42","title":"A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013","year":2015,"lang":"en","type":"article","venue":"Scientific Data","topic":"Climate variability and models","field":"Environmental Science","cited_by":424,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bureau of Reclamation; Instituto Tecnológico de Sonora; National Science Foundation","keywords":"Hydrometeorology; Precipitation; Orographic lift; Orography; Evapotranspiration; Environmental science; Climatology; Meteorology; Geography; Geology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002461504,0.0004981089,0.0005255049,0.004855618,0.001388366,0.0009299701,0.0008780478,0.0002725969,0.005136279],"category_scores_gemma":[0.001258225,0.0002636688,0.0002938417,0.01059379,0.0002982142,0.0003367091,0.0005394683,0.0004728975,0.001107518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008942974,"about_ca_system_score_gemma":0.01667666,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9702899,"about_ca_topic_score_gemma":0.9795869,"domain_scores_codex":[0.9997589,0.000007798423,0.00002115618,0.00005576262,0.0001053733,0.00005091888],"domain_scores_gemma":[0.9986331,0.00004591098,0.0002326932,0.00009089248,0.0008555786,0.0001417117],"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":[0.0002528118,0.0002151845,0.4257286,0.0009260533,0.0004038766,0.0004294777,0.001045432,0.01298834,0.00196298,0.004047103,0.4837273,0.06827284],"study_design_scores_gemma":[0.00004484574,0.00001579754,0.7390649,0.0001681543,0.00008110987,0.000118875,0.0006920279,0.00243824,0.0009220855,0.0002589841,0.2561437,0.00005127522],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06105962,0.0003562073,0.0006023556,0.0001248396,0.000019116,0.00008979118,0.9321843,0.0002556532,0.005308121],"genre_scores_gemma":[0.08095244,0.0006285561,0.003349768,0.00005551082,0.00001730024,0.0002302356,0.9107279,0.0000562217,0.003982178],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02971005,"threshold_uncertainty_score":0.06488615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1784396269167633,"score_gpt":0.2946546232915195,"score_spread":0.1162149963747562,"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."}}