{"id":"W4247328651","doi":"10.5194/esd-2020-10","title":"Multivariate bias corrections of climate simulations: Which benefits for which losses?","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"European Commission","keywords":"Univariate; Multivariate statistics; Variable (mathematics); Computer science; Climate model; Climate change; Econometrics; Data mining; Statistics; Machine learning; 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":[0.007278531,0.001102062,0.0006306436,0.001135097,0.000456792,0.001486077,0.0009139208,0.0007625932,0.001975953],"category_scores_gemma":[0.03548056,0.0002776752,0.0008846999,0.001210071,0.000600786,0.002183579,0.00146661,0.001591069,0.0004091087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005840289,"about_ca_system_score_gemma":0.001389693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004159429,"about_ca_topic_score_gemma":0.005216737,"domain_scores_codex":[0.9971198,0.001500473,0.0001609783,0.0003457481,0.0007246631,0.0001484486],"domain_scores_gemma":[0.9817759,0.007628706,0.002454021,0.00343813,0.004264574,0.0004385606],"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":[0.0007989454,0.000318294,0.06088816,0.00070442,0.000845637,0.0001697333,0.0004793696,0.3308373,0.01567557,0.02183812,0.007445543,0.5599989],"study_design_scores_gemma":[0.00008215576,0.0002461406,0.02280406,0.0003479226,0.0002852854,0.0001404921,0.0002803328,0.9307773,0.01660192,0.01840462,0.00990612,0.0001236124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1906438,0.003355618,0.7925452,0.003445111,0.0007562586,0.0001928004,0.0005100989,0.002553634,0.005997488],"genre_scores_gemma":[0.802985,0.001129333,0.1928217,0.0003508682,0.0003241949,0.0001148028,0.0003797727,0.000636122,0.001258175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007278531,"threshold_uncertainty_score":0.03849304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09558473508147397,"score_gpt":0.3149381379081422,"score_spread":0.2193534028266682,"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."}}