{"id":"W2808149924","doi":"10.1007/s10661-018-6769-1","title":"Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches","year":2018,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Water Systems and Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial neural network; Support vector machine; Perceptron; Residual; Multilayer perceptron; Data mining; Mean squared error; Artificial intelligence; Computer science; Machine learning; Statistics; Mathematics; Algorithm","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":[0.0002028293,0.00007759246,0.0001109582,0.00003670809,0.0001035887,0.00001861809,0.00007537646,0.00002397291,0.000002199811],"category_scores_gemma":[3.242197e-7,0.00005356107,0.00001802544,0.00009776504,0.0000618082,0.0001378001,0.0001159837,0.00004336155,1.042222e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005345837,"about_ca_system_score_gemma":8.045227e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001452239,"about_ca_topic_score_gemma":0.000001008986,"domain_scores_codex":[0.9994555,0.00001922411,0.0001926808,0.0001190558,0.0001128923,0.0001006755],"domain_scores_gemma":[0.9997168,0.000007590198,0.00004972576,0.0002055973,0.000002365199,0.00001789291],"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":[0.000001029805,0.00001367358,0.3789717,0.00005157508,0.0003390852,7.284756e-8,0.0002789838,0.6135039,0.001605344,0.000004202179,0.000003144481,0.005227339],"study_design_scores_gemma":[0.00005220267,0.00001742644,0.2504654,0.00004114837,0.0002315171,3.577335e-7,0.0003328515,0.7430298,0.00575668,3.88368e-7,0.00002395499,0.00004825344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9597878,0.00005011004,0.03977785,0.000002213734,0.0001900107,0.00007751765,0.00001144406,0.0000118491,0.00009117556],"genre_scores_gemma":[0.9962073,0.00006827016,0.003452904,2.818693e-7,0.0001012374,0.000004628147,0.0001495446,0.000007563826,0.000008275734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1295259,"threshold_uncertainty_score":0.2184157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03556896594372323,"score_gpt":0.2342343095026294,"score_spread":0.1986653435589062,"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."}}