{"id":"W2046433127","doi":"10.1007/s00477-013-0839-2","title":"Coupling fuzzy-chance constrained program with minimax regret analysis for water quality management","year":2013,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Water resources management and optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China; National Science Foundation","keywords":"Regret; Minimax; Fuzzy logic; Computer science; Quality (philosophy); Risk analysis (engineering); Computational intelligence; Environmental economics; Operations research; Risk management; Management science; Mathematical optimization; Business; Economics; Engineering; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005711433,0.0002052995,0.000233701,0.0002467555,0.0002880234,0.0001988934,0.000138325,0.00004890171,0.0001047357],"category_scores_gemma":[0.000003433356,0.0001413822,0.00005697207,0.0001793181,0.000246423,0.0001636327,0.0001229022,0.000194428,0.00001805936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001465623,"about_ca_system_score_gemma":0.0000032762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004330814,"about_ca_topic_score_gemma":0.0000223706,"domain_scores_codex":[0.9981878,0.00003614878,0.0002592184,0.000383153,0.0005272521,0.0006063937],"domain_scores_gemma":[0.999442,0.0000824205,0.00003721332,0.0002529985,0.00002232293,0.0001630791],"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":[0.0001584092,0.0005378255,0.03211176,0.0004320061,0.00335842,0.00001005347,0.0006043856,0.9330263,0.0006673191,0.000662914,0.0002276068,0.02820301],"study_design_scores_gemma":[0.001780868,0.0008747415,0.06177739,0.00004386046,0.0003947465,9.820552e-7,0.002219036,0.9305979,0.0003181573,0.001022883,0.0005210586,0.0004483917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.681727,0.00009612916,0.3142567,0.00007510172,0.000027112,0.002688716,0.00005070537,0.000108246,0.0009703615],"genre_scores_gemma":[0.9767594,0.0003347768,0.02047731,0.000004077758,0.0000304747,0.001474399,0.000312754,0.00003152692,0.0005753092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2950324,"threshold_uncertainty_score":0.5765398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106447282188643,"score_gpt":0.3023735505420747,"score_spread":0.2813090777201883,"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."}}