{"id":"W4225794124","doi":"10.1109/tase.2022.3163407","title":"Combined Dual-Prediction Based Data Fusion and Enhanced Leak Detection and Isolation Method for WSN Pipeline Monitoring System","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leak; Wireless sensor network; Real-time computing; Computer science; Transmission (telecommunications); Sensor fusion; Pipeline (software); Data transmission; Isolation (microbiology); Electric power transmission; Fusion center; Pipeline transport; Engineering; Wireless; Artificial intelligence; Computer network; Telecommunications","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":[0.000578515,0.0007071175,0.000794897,0.0004983192,0.0004085822,0.0004872214,0.0009231076,0.0005926667,0.0005798042],"category_scores_gemma":[0.001202589,0.0002775688,0.0005096355,0.0004734414,0.0002770479,0.00152699,0.001060897,0.0007875399,0.0001923093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004044638,"about_ca_system_score_gemma":0.0005620702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001841485,"about_ca_topic_score_gemma":0.001500607,"domain_scores_codex":[0.9993634,0.00006557222,0.00005466827,0.0001827502,0.0002739437,0.00005970931],"domain_scores_gemma":[0.9996164,0.00007962433,0.00006427699,0.00005709498,0.0001608982,0.00002175702],"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":[0.0007804528,0.0003111623,0.007577353,0.0003874395,0.0001249822,0.000384962,0.0004775937,0.2252075,0.1459162,0.002858073,0.002745939,0.6132283],"study_design_scores_gemma":[0.00001398435,0.0001407519,0.001079851,0.00001065967,0.00002006053,0.0001085083,0.00003321146,0.9682049,0.02858521,0.0008141054,0.0009672338,0.00002164681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04435597,0.0004137464,0.9529455,0.0001908207,0.000070756,0.00004075605,0.00005119296,0.0009732862,0.0009579318],"genre_scores_gemma":[0.879882,0.0003003194,0.1180025,0.0001221961,0.00003914019,0.00007364839,0.0001315112,0.00003312781,0.001415557],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001841485,"threshold_uncertainty_score":0.003661513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769131416949927,"score_gpt":0.2382639401373957,"score_spread":0.2205726259678965,"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."}}