{"id":"W3000741004","doi":"","title":"A New Algorithm for Blending Multiple Satellite Precipitation Estimates With In-situ Gauge Precipitation Measurements Over Canada","year":2009,"lang":"en","type":"article","venue":"AGUSM","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Precipitation; Satellite; In situ; Climatology; Algorithm; Meteorology; Environmental science; Quantitative precipitation estimation; Remote sensing; Gauge (firearms); Computer science; Geology; Materials science; Geography; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003378949,0.0001776417,0.0001965519,0.0001547488,0.0001449419,0.00008404081,0.0001327105,0.00004576814,0.00012657],"category_scores_gemma":[0.0001064856,0.000152472,0.00004543832,0.0003618865,0.00001054613,0.0004842476,0.000002181923,0.00007186084,0.00001121452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005139596,"about_ca_system_score_gemma":0.0002476884,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1530492,"about_ca_topic_score_gemma":0.8723877,"domain_scores_codex":[0.9984823,0.00004689434,0.0002884844,0.0003086764,0.0005436538,0.0003299719],"domain_scores_gemma":[0.9992898,0.0002037184,0.0001397175,0.0001235247,0.0001094501,0.0001337532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006833648,0.00001825996,0.4341246,0.00001122504,0.00004635817,0.000002521271,0.0004800949,0.002322352,0.0006899202,0.00001118671,0.000682504,0.5615426],"study_design_scores_gemma":[0.001246472,0.0001739765,0.968788,0.00007508372,0.00006096536,0.000001000615,0.0001286194,0.02522202,0.002680946,0.0007210698,0.00064969,0.0002521678],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9067938,0.003191988,0.07942554,0.001010256,0.0007210564,0.001847697,0.0001308684,0.0001482401,0.006730562],"genre_scores_gemma":[0.9495031,0.00002333152,0.04950245,0.0001761591,0.00008583617,0.000005722056,0.0004875489,0.000005184699,0.0002106178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7193385,"threshold_uncertainty_score":0.8525907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02598272173314117,"score_gpt":0.2349326442151718,"score_spread":0.2089499224820307,"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."}}