{"id":"W2999927402","doi":"","title":"An algorithm for blending multiple satellite precipitation estimates with in situ precipitation measurements in Canada","year":2012,"lang":"en","type":"article","venue":"EGUGA","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Precipitation; Gauge (firearms); Rain gauge; Satellite; Data set; Mean squared error; Algorithm; Kriging; Mathematics; Statistics; Meteorology; Environmental science; Remote sensing; Physics; Geology; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007671069,0.0001386875,0.0001677538,0.000191323,0.00009056794,0.0000442793,0.0001134332,0.00003386953,0.00008769538],"category_scores_gemma":[0.00009189842,0.0001216887,0.00002235366,0.0003474645,0.0000128902,0.000923781,0.000002236683,0.00006869114,0.00001036967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009830836,"about_ca_system_score_gemma":0.0001759765,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3151666,"about_ca_topic_score_gemma":0.9742724,"domain_scores_codex":[0.9986069,0.0001010726,0.0002779925,0.0002175979,0.000408172,0.0003882153],"domain_scores_gemma":[0.9993722,0.0002168001,0.0001058237,0.0001086175,0.00007504636,0.0001214852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002841873,0.00002702335,0.8577726,0.0000118456,0.00001386856,3.933565e-7,0.0009007284,0.005584822,0.0003842884,0.000001529603,0.00001328525,0.1352612],"study_design_scores_gemma":[0.0007135429,0.00006338198,0.9528118,0.00004271749,0.00002135441,4.776395e-7,0.0008083489,0.04309193,0.002113656,0.00006286606,0.00009119468,0.0001787802],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937671,0.0006333028,0.004194388,0.00005415444,0.0002458044,0.0004530101,0.00003552965,0.00002097141,0.0005956985],"genre_scores_gemma":[0.9714717,0.00001250884,0.02784946,0.0000421831,0.0000664588,0.00002009183,0.0005152987,0.000005817944,0.00001653785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6591058,"threshold_uncertainty_score":0.6893938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03772253437705543,"score_gpt":0.2452950580592924,"score_spread":0.207572523682237,"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."}}