{"id":"W2046156160","doi":"10.1002/2013wr014650","title":"Wavelet‐based multiscale performance analysis: An approach to assess and improve hydrological models","year":2014,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Computer science; Wavelet transform; Scale (ratio); Measure (data warehouse); Series (stratigraphy); Mean squared error; Data mining; Statistics; Mathematics; Artificial intelligence; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003558947,0.0001983902,0.0003023626,0.0002237646,0.0004958552,0.0002036085,0.0006640618,0.000172034,0.0001839879],"category_scores_gemma":[0.0000993511,0.0001211677,0.00006441271,0.0006716591,0.0005978353,0.0002157372,0.000810362,0.0005137968,0.0002897899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008708445,"about_ca_system_score_gemma":0.000002877685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003287841,"about_ca_topic_score_gemma":0.00002601543,"domain_scores_codex":[0.9960468,0.0007446771,0.0002504268,0.0009561181,0.0009961197,0.001005824],"domain_scores_gemma":[0.9987237,0.0001253355,0.00002581422,0.0005990113,0.00002864388,0.0004974494],"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":[0.0002011889,0.0004408815,0.03405261,0.00003062362,0.00004361293,0.000006389696,0.002561197,0.9163473,0.0288329,0.0000220382,0.00006036415,0.01740086],"study_design_scores_gemma":[0.0002345653,0.0005469318,0.01399661,0.000003894271,0.00002086358,0.000002620592,0.00001853489,0.9788417,0.003211634,0.0002421647,0.002678811,0.0002016834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980892,0.000003352873,0.006606798,0.0002574658,0.00001139684,0.0003041173,0.000003371376,0.0000824617,0.01183903],"genre_scores_gemma":[0.9884737,9.969584e-7,0.01044022,0.0002631702,0.00005382437,0.00008080886,0.00002140446,0.00002062747,0.0006452396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06249436,"threshold_uncertainty_score":0.4941077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1154676127361794,"score_gpt":0.3194811420773527,"score_spread":0.2040135293411733,"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."}}