{"id":"W4220912739","doi":"10.5194/egusphere-egu22-10519","title":"Adaptive Bias Correction for Improved Subseasonal Forecasting","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Predictability; Environmental science; Meteorology; Global Forecast System; Precipitation; Climatology; Weather forecasting; Climate model; Numerical weather prediction; Computer science; Climate change; Mathematics; Statistics; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0009785836,0.0007711768,0.00068336,0.0008805962,0.0004892505,0.0007745266,0.001204644,0.0008768573,0.005181979],"category_scores_gemma":[0.0048162,0.0003558382,0.0005990449,0.001436119,0.0002922715,0.001065789,0.0009889506,0.001562929,0.002054728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007487722,"about_ca_system_score_gemma":0.001570938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02587185,"about_ca_topic_score_gemma":0.03015509,"domain_scores_codex":[0.9996012,0.00006850347,0.00002312555,0.0001050647,0.0001472355,0.00005490342],"domain_scores_gemma":[0.9990487,0.0002234935,0.00006576866,0.0001704257,0.0004577833,0.00003386265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000325603,0.00008908357,0.003292389,0.00009406346,0.0001054172,0.000103885,0.00009986759,0.3821793,0.01535636,0.0115066,0.01666632,0.5701812],"study_design_scores_gemma":[0.000006309215,0.000008995476,0.000402411,0.000007436965,0.000006785565,0.00001126354,0.000005384559,0.9931098,0.001619499,0.001469086,0.003343679,0.000009417554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02393886,0.001044941,0.9653493,0.0005956431,0.0007324575,0.0000386548,0.00047068,0.002577066,0.005252475],"genre_scores_gemma":[0.4848605,0.0009604597,0.488311,0.0006095974,0.0003726062,0.00009864244,0.002113821,0.000725415,0.02194804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02587185,"threshold_uncertainty_score":0.0514425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1592274284485589,"score_gpt":0.2683228047702412,"score_spread":0.1090953763216822,"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."}}