{"id":"W4387360004","doi":"10.1016/j.catena.2023.107571","title":"A comparison of annual streamflow sensitivities to vegetation change and climate variability in fourteen large watersheds along climate zones in China","year":2023,"lang":"en","type":"article","venue":"CATENA","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Science Fund for Distinguished Young Scholars of Sichuan Province; Natural Sciences and Engineering Research Council of Canada","keywords":"Streamflow; Environmental science; Climate change; Temperate climate; Vegetation (pathology); Precipitation; Hydrology (agriculture); Watershed; Physical geography; Climatology; Drainage basin; Geology; Ecology; Geography; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001194884,0.0001184798,0.0002598756,0.0001326178,0.00007940224,0.000007362079,0.00006834302,0.00005206507,0.00001291888],"category_scores_gemma":[0.00003392847,0.0001045586,0.00001911816,0.0003087768,0.0001097952,0.0002180707,0.0003995856,0.00007814149,0.00006752904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004491954,"about_ca_system_score_gemma":0.000001193919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006981194,"about_ca_topic_score_gemma":0.003817622,"domain_scores_codex":[0.9987987,0.0001486081,0.0002444365,0.0002779324,0.0001071638,0.0004231491],"domain_scores_gemma":[0.9997237,0.00006614167,0.00004737926,0.0001216554,0.000004121406,0.00003703934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004179422,0.00007863676,0.9704234,0.0001001073,0.000006984912,0.00001763673,0.02665105,0.0008699044,0.0001816633,0.0001167316,0.00004567433,0.001466403],"study_design_scores_gemma":[0.000334231,0.00006584365,0.9900835,0.00002857438,0.000009476179,5.41781e-7,0.001741496,0.006861067,0.000341585,0.0003739122,0.00005011346,0.000109641],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998082,0.00001340139,0.00003524608,0.0009439963,0.00007108808,0.0003263814,0.00003095795,0.00003684868,0.0004600717],"genre_scores_gemma":[0.9996403,0.00007392857,0.00008457468,0.00008548229,0.00001163743,0.00005431026,0.00002807508,0.000007513765,0.00001418052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02490955,"threshold_uncertainty_score":0.4263777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02253160244336603,"score_gpt":0.2742903153095623,"score_spread":0.2517587128661962,"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."}}