{"id":"W2749859467","doi":"10.1109/jsen.2017.2740220","title":"Pipeline Leak Detection by Using Time-Domain Statistical Features","year":2017,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China","keywords":"Leak; Pipeline transport; Pipeline (software); Computer science; Waveform; Leak detection; Feature extraction; Frequency domain; Time domain; SIGNAL (programming language); Feature (linguistics); Field (mathematics); Pattern recognition (psychology); Engineering; Data mining; Real-time computing; Artificial intelligence; Computer vision; Radar; Mathematics","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":[0.0002663829,0.0006618942,0.0004081647,0.001265545,0.0001453451,0.0004428614,0.0003393303,0.0003050297,0.0005226954],"category_scores_gemma":[0.001412691,0.0002070963,0.0004690809,0.001069765,0.000224654,0.000990674,0.0003469591,0.0003770864,0.0002742588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002362869,"about_ca_system_score_gemma":0.0004659929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001952219,"about_ca_topic_score_gemma":0.001502946,"domain_scores_codex":[0.9997194,0.00003090359,0.00001962049,0.00006442582,0.0001392604,0.00002621118],"domain_scores_gemma":[0.9994648,0.0001331854,0.0001536425,0.00004119082,0.0001913894,0.00001575365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003935827,0.0001978582,0.008712753,0.0001928825,0.00009167558,0.0002117291,0.0001225115,0.1372279,0.1721824,0.002224076,0.001997724,0.6764449],"study_design_scores_gemma":[0.00001123404,0.0001597709,0.007920708,0.000008280332,0.00004103734,0.0001305392,0.00004241307,0.9535684,0.0349805,0.001159223,0.001943703,0.00003406673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06253225,0.0001666889,0.9355975,0.00005844074,0.00002106394,0.0000295835,0.0001293174,0.0009489555,0.0005162539],"genre_scores_gemma":[0.828845,0.0003123449,0.1688753,0.0000353572,0.00003387025,0.00006004364,0.000538965,0.00009954859,0.001199542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001952219,"threshold_uncertainty_score":0.003881752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009773542201093725,"score_gpt":0.2282558580049333,"score_spread":0.2184823158038396,"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."}}