{"id":"W4408545256","doi":"10.1061/jhyeff.heeng-6499","title":"Discussion of “Hybrid Multivariate Machine Learning Models for Streamflow Forecasting: A Two-Stage Decomposition–Reconstruction Framework”","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Université Laval","funders":"","keywords":"Streamflow; Multivariate statistics; Stage (stratigraphy); Decomposition; Computer science; Artificial intelligence; Multivariate analysis; Machine learning; Hydrology (agriculture); Econometrics; Mathematics; Geology; Geography; Geotechnical engineering; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003427335,0.0001219508,0.0002619738,0.0002332883,0.00008163418,0.00004162071,0.0002638225,0.00003702002,0.000002893579],"category_scores_gemma":[0.0003081822,0.00008481932,0.0001282774,0.0001773938,0.00001405161,0.0003202815,0.00007938984,0.0002893606,1.707514e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004266008,"about_ca_system_score_gemma":0.00004051175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004633136,"about_ca_topic_score_gemma":2.059872e-7,"domain_scores_codex":[0.9989674,0.00003270838,0.0005162617,0.0001520487,0.0001617134,0.0001698519],"domain_scores_gemma":[0.9988002,0.0005984864,0.0002798043,0.00008666884,0.0001797624,0.00005510041],"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":[0.00003451492,0.00002873441,0.00008636538,0.00004725412,0.0000410193,0.000009349447,0.0000520011,0.9362929,0.001241167,0.04428739,0.000001585313,0.01787765],"study_design_scores_gemma":[0.0004575046,0.0001519736,0.00003082741,0.0002394811,0.00001814862,0.00009612501,0.000006938619,0.8944385,0.001080843,0.1033859,0.00001493029,0.00007881904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06477462,0.0001510188,0.9342652,0.0002744472,0.0003762258,0.00008065071,0.000005688295,0.0000344434,0.00003771433],"genre_scores_gemma":[0.5909654,0.000005305167,0.4089597,0.00001770137,0.00003472571,0.000002846761,0.000001327047,0.000003738304,0.000009213682],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5261908,"threshold_uncertainty_score":0.3458832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02531593553030772,"score_gpt":0.2749500318004074,"score_spread":0.2496340962700997,"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."}}