{"id":"W4387437512","doi":"10.5194/hess-27-1865-2023","title":"Hybrid forecasting: blending climate predictions with AI models","year":2023,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":217,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; University of Saskatchewan","funders":"U.S. Army Corps of Engineers; Global Water Futures; Natural Environment Research Council; Sight Research UK; Swiss Federal Institute for Forest, Snow and Landscape Research; Science Foundation Ireland; Canada First Research Excellence Fund; UK Research and Innovation","keywords":"Predictability; Merge (version control); Computer science; Data assimilation; Numerical weather prediction; Forcing (mathematics); Forecast skill; Ensemble forecasting; Climate model; Environmental science; Meteorology; Climatology; Machine learning; Climate change; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008882921,0.0001162183,0.0001848511,0.0001841936,0.001280565,0.00008072944,0.0001465178,0.00004913241,0.0001680194],"category_scores_gemma":[0.00001539065,0.00007409136,0.00002593988,0.0004697259,0.0004215458,0.0003758697,0.00001966521,0.0001069024,0.0001203789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001195768,"about_ca_system_score_gemma":0.00002960001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001187774,"about_ca_topic_score_gemma":0.0002086863,"domain_scores_codex":[0.9986164,0.0001115194,0.0002032364,0.0003674702,0.0002050502,0.0004963255],"domain_scores_gemma":[0.9993846,0.000289698,0.000068284,0.0001012512,0.00001899124,0.0001372204],"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":[0.00002661928,0.000003878738,0.1650322,0.00002911609,0.00001209794,0.0000332645,0.0001322806,0.8251045,0.000003334182,0.007616109,0.00004932795,0.001957246],"study_design_scores_gemma":[0.0001469576,0.000370623,0.03330607,0.00001954717,0.00001336689,0.0001350399,0.0002522315,0.962562,0.000002604586,0.002870695,0.0002209504,0.0000999523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9634867,0.0001539658,0.00110087,0.0003602301,0.0002399727,0.0001611037,0.0000564737,0.0002350948,0.03420553],"genre_scores_gemma":[0.999347,0.00003199354,0.0002847437,0.0001403503,0.00006539262,0.000003912341,0.00002812407,0.000001893041,0.00009663917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1374575,"threshold_uncertainty_score":0.9849197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05696421831380235,"score_gpt":0.2328789758348111,"score_spread":0.1759147575210087,"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."}}