{"id":"W3158113899","doi":"10.2166/wcc.2021.293","title":"Assessing watershed hydrological response to climate change based on signature indices","year":2021,"lang":"en","type":"article","venue":"Journal of Water and Climate Change","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Watershed; Climate change; Surface runoff; Structural basin; Signature (topology); Hydrology (agriculture); Representative Concentration Pathways; Physical geography; Climatology; Climate model; Geology; Geography; Ecology; Mathematics; Computer science; Oceanography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005055305,0.0002421523,0.0002024359,0.001680484,0.0001825127,0.0006501548,0.0002048213,0.000239694,0.0005348519],"category_scores_gemma":[0.001332309,0.0000862356,0.0002696174,0.001901106,0.000188789,0.000508555,0.0003116769,0.0002198991,0.00008246562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007216398,"about_ca_system_score_gemma":0.0007036311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02011158,"about_ca_topic_score_gemma":0.02575345,"domain_scores_codex":[0.999792,0.00004187186,0.00001402697,0.00002968582,0.00008467412,0.00003768901],"domain_scores_gemma":[0.9995208,0.0001069016,0.0001223181,0.00003475345,0.0001538496,0.00006138788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009178506,0.00009049055,0.915569,0.00002679154,0.00009435751,0.0001172944,0.00007660157,0.05543689,0.003259088,0.0003318641,0.0005588288,0.024347],"study_design_scores_gemma":[0.000009544453,0.00008836934,0.8064528,0.000008963721,0.00004545776,0.00004842221,0.0002879048,0.189846,0.002145822,0.0004391028,0.0006078417,0.00001966746],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972651,0.00004277512,0.001143131,0.00004177101,0.000003849985,0.00001308716,0.0006688016,0.00005106731,0.0007705318],"genre_scores_gemma":[0.9986501,0.00003035416,0.0007042752,0.000004307764,0.000002963245,0.000005407343,0.0005264991,0.000002493271,0.00007354445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02011158,"threshold_uncertainty_score":0.03998905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04225257524923232,"score_gpt":0.2810021240251839,"score_spread":0.2387495487759516,"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."}}