{"id":"W2466875295","doi":"10.1007/s11269-016-1370-2","title":"Optimization and Evaluation of Environmental Operations for Three Gorges Reservoir","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China","keywords":"TOPSIS; Analytic hierarchy process; Three gorges; Water quality; Flood control; Computer science; Tributary; Multiple-criteria decision analysis; Environmental science; Water resources; Fuzzy logic; Flood myth; Operations research; Engineering; Artificial intelligence","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":[0.0007000927,0.0006927658,0.0009899433,0.0008141421,0.0006114539,0.001171027,0.0005876852,0.0009747248,0.001776763],"category_scores_gemma":[0.001077004,0.00050732,0.0009843868,0.0006851236,0.0005036194,0.0006649145,0.0004730374,0.0005596528,0.0001131612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348214,"about_ca_system_score_gemma":0.001423503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02500827,"about_ca_topic_score_gemma":0.01705746,"domain_scores_codex":[0.999752,0.00008698594,0.00001160106,0.0000331599,0.00004627417,0.00007009636],"domain_scores_gemma":[0.9995267,0.0002979872,0.00003769618,0.00002388732,0.00007550739,0.00003819477],"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":[0.00006566677,0.00005409076,0.00062987,0.00002622685,0.00001530889,0.00004508681,0.000008505163,0.9954965,0.0008713724,0.0002663502,0.00009303245,0.002428035],"study_design_scores_gemma":[0.00001141754,0.00009255084,0.0008308903,0.000001642659,0.00001228089,0.000004863762,0.00002394567,0.9981837,0.000643121,0.0001097447,0.00008105607,0.000004758485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760748,0.0002372276,0.01742194,0.0001657666,0.00002513009,0.00007039902,0.0002105046,0.0001125369,0.005681712],"genre_scores_gemma":[0.9959183,0.00006088727,0.003139075,0.000007009562,0.000002346018,0.0000361287,0.00008034184,0.00001095763,0.0007448281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02500827,"threshold_uncertainty_score":0.04972541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456356226468965,"score_gpt":0.2030872019948803,"score_spread":0.1885236397301906,"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."}}