{"id":"W2795869923","doi":"10.3390/w10040428","title":"Identification of Factors That Influence Energy Performance in Water Distribution System Mains","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jacobs (Canada); Queen's University","funders":"Engineering and Physical Sciences Research Council","keywords":"Leakage (economics); Mains electricity; Environmental science; Energy (signal processing); Electricity; Asset management; Pipe network analysis; Principal component analysis; Marine engineering; Engineering; Computer science; Statistics; Mathematics; Mechanics","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":[0.0005202309,0.0003833709,0.0003532035,0.001045281,0.0002991531,0.0008542117,0.0001411667,0.000257498,0.0009891028],"category_scores_gemma":[0.002911409,0.0002123117,0.0002964338,0.001137738,0.0003743975,0.0007919355,0.000392658,0.0002772374,0.0002022572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000411777,"about_ca_system_score_gemma":0.0003901577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006752597,"about_ca_topic_score_gemma":0.01033156,"domain_scores_codex":[0.9996883,0.00007687421,0.00002130103,0.00006179014,0.0001012162,0.00005052783],"domain_scores_gemma":[0.9987137,0.0007137235,0.0002007042,0.00007170464,0.0002479943,0.00005214146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004676926,0.0001756479,0.7143966,0.0001298834,0.0002187179,0.0003578549,0.0005893389,0.1604656,0.03488525,0.0008854605,0.001007928,0.08641993],"study_design_scores_gemma":[0.000005134955,0.0001587026,0.8404279,0.00001199364,0.00003789232,0.0000862637,0.000684916,0.1515061,0.005566329,0.0006859011,0.0008012725,0.00002775123],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929776,0.00003805279,0.005727724,0.00003969828,0.000002656167,0.00001632315,0.0002026879,0.00004584167,0.0009494812],"genre_scores_gemma":[0.9989225,0.00001922943,0.0006991378,0.000002241554,0.000001871028,0.000003799743,0.0001356304,0.000009106344,0.0002064841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006752597,"threshold_uncertainty_score":0.0134266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005302357024908943,"score_gpt":0.1656165913865544,"score_spread":0.1603142343616454,"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."}}