{"id":"W1554506217","doi":"10.14796/jwmm.r207-09","title":"Water Quality Management using a Fuzzy Inference System","year":2001,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fuzzy inference system; Fuzzy inference; Inference; Adaptive neuro fuzzy inference system; Water quality; Fuzzy logic; Quality (philosophy); Computer science; Fuzzy control system; Petroleum engineering; Environmental science; Engineering; Artificial intelligence; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001002351,0.0004822226,0.0007282166,0.0006414347,0.0009459345,0.001385479,0.00109738,0.001044056,0.002715789],"category_scores_gemma":[0.002048108,0.0002799808,0.0005468644,0.0005526993,0.0004446085,0.0009117433,0.0005204662,0.0008049257,0.0006066852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009739854,"about_ca_system_score_gemma":0.001157605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01631996,"about_ca_topic_score_gemma":0.01096742,"domain_scores_codex":[0.9994799,0.00008229953,0.00005239374,0.0001525419,0.0001883835,0.00004449314],"domain_scores_gemma":[0.9993967,0.0002191414,0.00007150734,0.00005608245,0.0002299589,0.00002663061],"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":[0.0002840161,0.0002287213,0.002693879,0.0001982176,0.0001375995,0.0002014374,0.0001512846,0.7005486,0.01482204,0.008632427,0.00247192,0.2696298],"study_design_scores_gemma":[0.00002902514,0.00005932206,0.0003527094,0.00001506258,0.00003755784,0.00002220787,0.000008874016,0.9944729,0.00170261,0.002184397,0.001102013,0.00001327417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0257587,0.0002815758,0.9658432,0.0002121055,0.00007687091,0.0001416123,0.0001538146,0.002255152,0.005276986],"genre_scores_gemma":[0.7879094,0.0002726703,0.2079867,0.0001125029,0.00007020011,0.0002668119,0.0002192376,0.00003445056,0.003128074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01631996,"threshold_uncertainty_score":0.03244996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0826521859099599,"score_gpt":0.3036572494755633,"score_spread":0.2210050635656034,"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."}}