{"id":"W2607097720","doi":"10.1007/s00521-017-2987-7","title":"Extreme learning machine model for water network management","year":2017,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":146,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Extreme learning machine; Artificial neural network; Computer science; Support vector machine; Backpropagation; Water pipe; Artificial intelligence; Machine learning; Failure rate; Reliability engineering; Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001075494,0.000529263,0.001294681,0.0004522919,0.0005635381,0.00123008,0.00197417,0.001965904,0.004966447],"category_scores_gemma":[0.002307519,0.000327753,0.0005889511,0.0008486624,0.0006298297,0.001412325,0.001013851,0.0020302,0.0005796244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009272074,"about_ca_system_score_gemma":0.00102657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006795398,"about_ca_topic_score_gemma":0.00490089,"domain_scores_codex":[0.9995415,0.0001903953,0.00001873042,0.0000917221,0.00009427183,0.00006343034],"domain_scores_gemma":[0.9994417,0.0002760312,0.00005166391,0.00003450125,0.0001631642,0.00003291317],"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":[0.00002088958,0.00002289092,0.0001438683,0.00001769555,0.00001495315,0.00002268445,0.00001017103,0.977801,0.0001502368,0.00908008,0.001197831,0.01151763],"study_design_scores_gemma":[0.000001962709,0.000004402277,0.00002727862,0.000001306608,0.000001786865,0.000001874613,0.000001628866,0.9944069,0.00003262529,0.005334023,0.0001843965,0.000001727864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01169902,0.0003313589,0.9827617,0.0004803489,0.0001038526,0.00003304539,0.000115593,0.000201201,0.004273878],"genre_scores_gemma":[0.8849418,0.0005065187,0.08741274,0.0003094246,0.0001982331,0.0002721075,0.0004209153,0.00009247022,0.02584588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006795398,"threshold_uncertainty_score":0.01661438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03348925244356096,"score_gpt":0.2878099477885384,"score_spread":0.2543206953449774,"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."}}