{"id":"W4367145160","doi":"10.1121/10.0018930","title":"Maximum likelihood estimation for leak localization in water distribution networks using in-pipe acoustic sensing","year":2023,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Leak; Pipe network analysis; Leak detection; Computer science; Pipeline transport; Hydrophone; Acoustics; Pipeline (software); Wireless sensor network; Real-time computing; Noise (video); Environmental science; Artificial intelligence; Computer network; Physics","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":[0.001536589,0.001197633,0.001152794,0.0007005465,0.0003233782,0.0007483681,0.001075459,0.00109929,0.001200596],"category_scores_gemma":[0.007332487,0.000847319,0.0006616325,0.0008757439,0.001061334,0.001771526,0.001094469,0.001097858,0.0004151452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000769646,"about_ca_system_score_gemma":0.0009831069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004826196,"about_ca_topic_score_gemma":0.003409877,"domain_scores_codex":[0.9993532,0.0003285643,0.00002776347,0.0001277785,0.00009920243,0.0000634257],"domain_scores_gemma":[0.9967735,0.002596239,0.0002587283,0.00009115226,0.0002231924,0.0000572639],"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":[0.00007415729,0.00002821016,0.0008692521,0.00006911745,0.00003176588,0.00005219767,0.00004616728,0.9775125,0.001212235,0.00318701,0.0004025271,0.01651471],"study_design_scores_gemma":[0.000004592039,0.00001126845,0.000126847,0.000004017489,0.000002462845,0.00000596209,0.000005848501,0.9977946,0.0002083684,0.001732999,0.00009860015,0.000004386767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01164733,0.0001740351,0.987402,0.0001397179,0.0000112419,0.00001881943,0.00004659675,0.0001454794,0.0004147953],"genre_scores_gemma":[0.7616606,0.0006584647,0.232892,0.0001368521,0.00008647617,0.0002336456,0.0004535731,0.0001054166,0.003772948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004826196,"threshold_uncertainty_score":0.009596169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009980180537311385,"score_gpt":0.2233123384438698,"score_spread":0.2133321579065584,"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."}}