{"id":"W4404637911","doi":"10.5194/egusphere-2024-3170","title":"Benchmarking historical performance and future projections from a global hydrologic model with a basin-scale model","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Coupled model intercomparison project; Evapotranspiration; Hydrological modelling; Climatology; Watershed; Benchmarking; Water balance; Hydrology (agriculture); Climate change; Climate model; Computer science; Ecology; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001433866,0.0007610621,0.0003628721,0.0004464381,0.0002453426,0.0006662256,0.0005938389,0.0006404934,0.0009548982],"category_scores_gemma":[0.00184358,0.0002037935,0.0007150011,0.0005823075,0.0003087867,0.0007214631,0.0004060738,0.0006202999,0.00017127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007368318,"about_ca_system_score_gemma":0.000879952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03898704,"about_ca_topic_score_gemma":0.02363198,"domain_scores_codex":[0.9997336,0.00008873329,0.00001692913,0.00007380998,0.00004973586,0.0000371779],"domain_scores_gemma":[0.9993124,0.0002438692,0.00006717566,0.0001358501,0.0001840246,0.00005660558],"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":[0.00009433136,0.00006750096,0.02161312,0.00002372618,0.0001056579,0.00003509335,0.00002022539,0.9721548,0.001055019,0.0003124333,0.0006242127,0.003893905],"study_design_scores_gemma":[0.00002786615,0.0000838729,0.01119858,0.000007257102,0.00002959535,0.00001160702,0.00002502613,0.9868978,0.001134012,0.0001865572,0.0003826793,0.0000151458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916894,0.00007694876,0.004063702,0.000131401,0.00002559979,0.00001702569,0.001684886,0.0003068133,0.002004025],"genre_scores_gemma":[0.9942274,0.00004145785,0.002887571,0.00002126445,0.000007387972,0.00001659688,0.002590901,0.00004417536,0.0001631644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03898704,"threshold_uncertainty_score":0.07752019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01154112691328515,"score_gpt":0.2060665902824028,"score_spread":0.1945254633691176,"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."}}