{"id":"W2028418930","doi":"10.13031/2013.39845","title":"Technical Note: Enhancement of SWAT-REMM to Simulate Reduction of Total Nitrogen with Riparian Buffer","year":2011,"lang":"en","type":"article","venue":"Transactions of the ASABE","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Environment","keywords":"Riparian zone; Environmental science; Riparian buffer; Nonpoint source pollution; Watershed; SWAT model; Pollution; Hydrology (agriculture); Environmental engineering; Computer science; Engineering; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001187623,0.00007799356,0.0001380279,0.00002940251,0.00007457581,8.353992e-7,0.0001623037,0.00003729559,0.0005528464],"category_scores_gemma":[0.000003384114,0.00005177472,0.00005470626,0.0001747835,0.0002943898,0.00007069555,0.00003547933,0.00006619184,0.00001891001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002753975,"about_ca_system_score_gemma":0.000003475135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004873146,"about_ca_topic_score_gemma":0.00008730243,"domain_scores_codex":[0.9993775,0.00002762835,0.0001898234,0.0001362566,0.0001499625,0.0001187812],"domain_scores_gemma":[0.9996024,0.000009675324,0.00007609953,0.0002776317,0.000009927046,0.0000242018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001550376,0.002033417,0.01460108,0.0001011285,0.0004130621,0.00000147294,0.008784022,0.09259004,0.8700026,0.0001920281,0.0005648788,0.009165866],"study_design_scores_gemma":[0.000516655,0.0007738484,0.02362405,0.00002963694,0.0002707613,0.000004786384,0.000224121,0.0002471682,0.9732419,0.0007762855,0.0001512464,0.0001395706],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9186893,0.000005523312,0.0759614,0.0004482556,0.00008551696,0.0003261625,0.000004959606,0.00001283341,0.004466085],"genre_scores_gemma":[0.9954545,0.000004274991,0.003902416,0.00001902719,0.000003792851,0.00001664521,3.378688e-7,0.000005182671,0.0005938085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1032392,"threshold_uncertainty_score":0.605328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01321659503727054,"score_gpt":0.2196453894266216,"score_spread":0.2064287943893511,"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."}}