{"id":"W4408945607","doi":"10.1016/j.jenvman.2025.125091","title":"Assessing climate change and human impacts on runoff and hydrological droughts in the Yellow River Basin using a machine learning-enhanced hydrological modeling approach","year":2025,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"China Institute of Water Resources and Hydropower Research; National Natural Science Foundation of China","keywords":"Climate change; Surface runoff; Environmental science; Hydrology (agriculture); Structural basin; Drainage basin; Water resource management; Hydrological modelling; Water cycle; Environmental resource management; Geography; Climatology; Ecology; Geology; Oceanography; Geomorphology; 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":[0.001178361,0.0002289634,0.0003396918,0.0001615134,0.0003819847,0.00007693131,0.0002343767,0.0001175961,0.00008202715],"category_scores_gemma":[0.000008794485,0.0001511793,0.00009597142,0.0001841466,0.0003368852,0.0004003378,0.0003986758,0.0005097355,0.00000587169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001685187,"about_ca_system_score_gemma":0.000001275257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005549618,"about_ca_topic_score_gemma":0.00001333948,"domain_scores_codex":[0.9981479,0.0003618377,0.0004318951,0.0003606236,0.0003410298,0.0003567252],"domain_scores_gemma":[0.999496,0.00005887568,0.0002120406,0.0001571988,0.000001147582,0.0000746943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006872676,0.002635092,0.6468959,0.0001158012,0.0004119084,0.0008271125,0.003590528,0.3149042,0.0150065,0.0008898004,0.00002730509,0.0140086],"study_design_scores_gemma":[0.002103433,0.0006529882,0.4207204,0.0001142772,0.0004007382,0.00009606556,0.0008933367,0.5719786,0.000109259,0.002323102,0.0002558976,0.0003518727],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993873,0.0002407975,0.00125809,0.0003475762,0.00002791544,0.0002420648,0.000001099527,0.000006733148,0.004002711],"genre_scores_gemma":[0.9969937,0.0005797245,0.001387222,0.0009520296,0.00003220584,0.000008350763,0.00000298606,0.000008590121,0.00003522059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2570745,"threshold_uncertainty_score":0.6164913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02989235076982499,"score_gpt":0.2693789676808478,"score_spread":0.2394866169110228,"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."}}