{"id":"W2170960572","doi":"10.1061/(asce)wr.1943-5452.0000564","title":"Performance Index for Water Distribution Networks under Multiple Loading Conditions","year":2015,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Redundancy (engineering); Reliability engineering; Computer science; Index (typography); Metric (unit); Efficient energy use; Sensitivity (control systems); Resilience (materials science); Range (aeronautics); Reliability (semiconductor); Performance indicator; Performance metric; Data mining; Engineering","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":[0.00261184,0.001028836,0.0007002098,0.004610815,0.0004781772,0.001274509,0.0007351856,0.0007687565,0.001616909],"category_scores_gemma":[0.008152165,0.0001309399,0.0005020131,0.002818366,0.000688489,0.002289718,0.001063617,0.0004359043,0.0003013613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001714607,"about_ca_system_score_gemma":0.0005529997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878118,"about_ca_topic_score_gemma":0.00146831,"domain_scores_codex":[0.99827,0.0004357009,0.0001451471,0.0002633896,0.0006803471,0.0002053429],"domain_scores_gemma":[0.9947128,0.002810362,0.0009925544,0.0004081881,0.0009222651,0.0001538371],"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":[0.0001681444,0.00008820008,0.01259862,0.0001316973,0.00008470689,0.0001455883,0.0001112533,0.9155158,0.004970149,0.009243233,0.001342795,0.05559986],"study_design_scores_gemma":[0.000007002965,0.0002799245,0.009463284,0.00001996268,0.00003083738,0.0001964664,0.000107058,0.9764847,0.005036005,0.006751782,0.001584916,0.00003816474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4999254,0.001071293,0.4729299,0.0004182986,0.0001108947,0.0002506703,0.001566472,0.001162545,0.02256444],"genre_scores_gemma":[0.9770233,0.0001766286,0.02119462,0.00001713597,0.00003121836,0.00007498058,0.0006586531,0.00004663533,0.0007767146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004610815,"threshold_uncertainty_score":0.0138129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01694908438788549,"score_gpt":0.211785518583182,"score_spread":0.1948364341952965,"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."}}