{"id":"W607998451","doi":"10.1016/j.ecolind.2015.05.031","title":"Analyses of landuse change impacts on catchment runoff using different time indicators based on SWAT model","year":2015,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":254,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Environment; Nipissing University; Ministry of the Environment, Conservation and Parks","funders":"National Natural Science Foundation of China","keywords":"Surface runoff; Environmental science; Hydrology (agriculture); Evapotranspiration; Runoff curve number; Interception; Flood myth; SWAT model; Streamflow; Land use; Runoff model; Drainage basin; Soil and Water Assessment Tool; Geography; Ecology","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":[0.0006074827,0.0003558151,0.0002440976,0.001095544,0.0002050998,0.0005128952,0.0001814428,0.0002624271,0.0008386197],"category_scores_gemma":[0.0008695862,0.0001135001,0.0007572063,0.001453968,0.0002359216,0.0006034373,0.0002620681,0.0002550572,0.00009082119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005006249,"about_ca_system_score_gemma":0.0004223176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0124214,"about_ca_topic_score_gemma":0.01609993,"domain_scores_codex":[0.9997681,0.00005100066,0.00001470487,0.00004378518,0.00006342203,0.00005910811],"domain_scores_gemma":[0.9994383,0.0002046712,0.00009641018,0.00003585512,0.0001568721,0.0000678428],"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":[0.001258498,0.0003741069,0.8669634,0.0001009236,0.0005799248,0.0003402605,0.0002881981,0.07443588,0.03021698,0.0008639659,0.001083761,0.02349408],"study_design_scores_gemma":[0.00001715452,0.0001318644,0.8909286,0.000003589894,0.0001549744,0.00004203884,0.0002899551,0.1021947,0.005536197,0.0001719704,0.0005146578,0.00001420764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985269,0.00002167338,0.0005129816,0.00001327269,0.000003342526,0.000005612218,0.0004003786,0.00002578611,0.0004900891],"genre_scores_gemma":[0.9986237,0.00003054303,0.0003681066,0.000003755903,0.000003161674,0.000007179777,0.0006300009,0.000009762474,0.0003238535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0124214,"threshold_uncertainty_score":0.0246982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1059930378330556,"score_gpt":0.3188840714926405,"score_spread":0.2128910336595849,"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."}}