{"id":"W2563460270","doi":"10.1016/j.scitotenv.2016.12.099","title":"An enhanced export coefficient based optimization model for supporting agricultural nonpoint source pollution mitigation under uncertainty","year":2016,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Water resources management and optimization","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland; University of Regina","funders":"National Key Research and Development Program of China; Key Technologies Research and Development Program; National Aerospace Science Foundation of China; National Science Foundation","keywords":"Credibility; Stochastic programming; Linear programming; Computer science; Nonpoint source pollution; Fuzzy logic; Mathematical optimization; Dual (grammatical number); Agriculture; Agricultural land; Environmental science; Land use; Operations research; Engineering; Mathematics; Civil engineering","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":[0.00067944,0.0008800493,0.00143905,0.000433892,0.0004473363,0.001264075,0.001335828,0.001900416,0.003104163],"category_scores_gemma":[0.001238243,0.0005875522,0.000796553,0.000599741,0.0005145589,0.0008900512,0.001050122,0.001113257,0.0002642524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008220707,"about_ca_system_score_gemma":0.001441074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01757167,"about_ca_topic_score_gemma":0.009228744,"domain_scores_codex":[0.9997726,0.00006711668,0.00001096107,0.00005749166,0.0000491604,0.00004266773],"domain_scores_gemma":[0.9995203,0.0002696037,0.00004841963,0.00002144976,0.0001147705,0.00002554184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000920608,0.000006737966,0.00005306223,0.000006162759,0.000004770066,0.00001158358,0.00000258875,0.9985349,0.0001322557,0.0005052788,0.00007306252,0.0006603085],"study_design_scores_gemma":[0.000002801391,0.00000402432,0.00001958308,6.033145e-7,0.000001747031,8.286127e-7,8.468481e-7,0.9997372,0.00003167689,0.0001480571,0.00005172869,9.254338e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1547918,0.0005694758,0.8180644,0.0007963989,0.0002026508,0.0001274581,0.000743693,0.0005543834,0.02414974],"genre_scores_gemma":[0.95836,0.0001930404,0.03297925,0.0001132188,0.00004427771,0.000162873,0.0003117227,0.00007989897,0.007755659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01757167,"threshold_uncertainty_score":0.03493881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007335505913031708,"score_gpt":0.1907434500896331,"score_spread":0.1834079441766014,"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."}}