{"id":"W3093017969","doi":"10.5194/hess-24-4887-2020","title":"Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada","year":2020,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Snow; Snowmelt; Mean squared error; Snowpack; Environmental science; Statistics; Linear regression; Regression; Meteorology; Mathematics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002899757,0.00005616401,0.0001596081,0.00002166646,0.0001494154,0.000005091139,0.00006843972,0.00002941098,0.0000476323],"category_scores_gemma":[0.00003033609,0.00003834634,0.00001653606,0.0001120174,0.000127269,0.00005273081,0.000008895945,0.00005107578,4.721735e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004068087,"about_ca_system_score_gemma":0.00007862202,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8973854,"about_ca_topic_score_gemma":0.9709595,"domain_scores_codex":[0.9993895,0.00002755327,0.0002047931,0.0001532282,0.0001002852,0.0001246304],"domain_scores_gemma":[0.9996287,0.0001908066,0.00009295352,0.00003119652,0.00002751908,0.00002877327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000299043,0.000002897704,0.9789058,0.00004359975,0.00000514227,3.521727e-7,0.0004255149,0.01693186,0.0001084302,0.00006184681,0.00004844166,0.00343621],"study_design_scores_gemma":[0.0001227342,0.0004170997,0.4236881,0.00003139515,0.000009633429,0.000007875236,0.0007475104,0.5688037,0.001624351,0.00003749736,0.004438691,0.00007150404],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997044,0.0002964608,0.00147342,0.0005587952,0.000112952,0.0001976788,0.00000873929,0.00001245796,0.0002955394],"genre_scores_gemma":[0.9991466,0.0000130188,0.0007040108,0.00006994508,0.00001405279,0.000004915867,0.00002550342,7.38915e-7,0.00002116983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5552177,"threshold_uncertainty_score":0.1563718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03857868212144688,"score_gpt":0.21995118629126,"score_spread":0.1813725041698131,"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."}}