{"id":"W4380872517","doi":"10.1038/s41467-023-38874-y","title":"Adaptive bias correction for improved subseasonal forecasting","year":2023,"lang":"en","type":"article","venue":"Nature Communications","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Oceanic and Atmospheric Administration; U.S. Department of Commerce; National Science Foundation","keywords":"Computer science; Econometrics; Mathematics","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":[0.001056664,0.0005383942,0.0004273486,0.0006880492,0.0003581965,0.0005952226,0.0007733314,0.0005105,0.001886816],"category_scores_gemma":[0.006309508,0.0001951468,0.0003341193,0.0008753221,0.0002357853,0.000783138,0.0008462737,0.0009674632,0.0005783518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005195356,"about_ca_system_score_gemma":0.001268873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03574371,"about_ca_topic_score_gemma":0.03406378,"domain_scores_codex":[0.9996688,0.00006820113,0.00002180924,0.00008800205,0.00009919998,0.00005406],"domain_scores_gemma":[0.9986761,0.0004021715,0.00009978781,0.0002390466,0.0005257538,0.00005714402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002244036,0.0001116969,0.02632637,0.00006114264,0.0001198715,0.0001112085,0.0001813994,0.4908061,0.01709269,0.006300656,0.01111574,0.4475488],"study_design_scores_gemma":[0.00001050061,0.000009392633,0.001878901,0.000004871695,0.000005410247,0.000009920012,0.00001068373,0.9930723,0.00194679,0.00143619,0.001605345,0.000009721805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.19271,0.0007096786,0.7947006,0.0009784099,0.0006151437,0.0000537141,0.000680655,0.004892686,0.004659029],"genre_scores_gemma":[0.7988586,0.0001770065,0.1970145,0.000207784,0.0001176027,0.00004045129,0.0007772828,0.000307383,0.00249945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03574371,"threshold_uncertainty_score":0.07107133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0728457282500125,"score_gpt":0.2742384693043667,"score_spread":0.2013927410543542,"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."}}