{"id":"W4409327502","doi":"10.1016/j.jhydrol.2025.133292","title":"Impact of extreme atmospheric heat events on river thermal dynamics and heatwaves","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"National Natural Science Foundation of China","keywords":"Environmental science; Atmospheric sciences; Climatology; Extreme heat; Heat wave; Thermal; Meteorology; Climate change; Geology; Oceanography; Geography","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":[0.0005754262,0.0002715745,0.0003176238,0.0004646217,0.0007553186,0.00158271,0.0002989313,0.0007450109,0.006150179],"category_scores_gemma":[0.001933348,0.0001894542,0.0006755276,0.0006801167,0.0005273985,0.0009348,0.001014716,0.0006679539,0.0004221035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006686138,"about_ca_system_score_gemma":0.000569372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01523662,"about_ca_topic_score_gemma":0.02129604,"domain_scores_codex":[0.9997254,0.00006849035,0.00001819142,0.00003657608,0.00003170478,0.0001196179],"domain_scores_gemma":[0.9989384,0.0004443968,0.0001550628,0.00005216787,0.0001426226,0.0002673812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002090773,0.0004700974,0.9538391,0.00007199466,0.0004373167,0.0008325074,0.0002602035,0.01991876,0.005635776,0.001334218,0.001775359,0.01333387],"study_design_scores_gemma":[0.00002486401,0.0001149995,0.9910868,0.000008471494,0.00006358749,0.00005606676,0.0005646066,0.006594148,0.0004256117,0.0003037208,0.00074686,0.00001032575],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963259,0.0000931025,0.0001149111,0.0002303287,0.00003447783,0.000004972235,0.0005745484,0.00001254538,0.002609135],"genre_scores_gemma":[0.9991123,0.00008216623,0.00003208617,0.00003399476,0.00002232812,0.000002463864,0.0002860249,0.000005671059,0.0004229206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01523662,"threshold_uncertainty_score":0.03029585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007474011291305198,"score_gpt":0.2293473733035821,"score_spread":0.2218733620122769,"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."}}