{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008603779,0.0001344647,0.0001331075,0.0004010335,0.0006161073,0.0001249481,0.00007755166,0.00004593463,0.000001931193],"category_scores_gemma":[0.00001344723,0.0001488667,0.00001402532,0.0004776747,0.00001982276,0.0007442391,0.000006617051,0.0001185801,3.308371e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001715997,"about_ca_system_score_gemma":0.00001816037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001579069,"about_ca_topic_score_gemma":0.00001666868,"domain_scores_codex":[0.9989615,0.0000223586,0.0002571326,0.0003121976,0.0002786322,0.0001681722],"domain_scores_gemma":[0.9995404,0.00007291512,0.00004337023,0.0001945018,0.00007663759,0.00007211441],"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":[0.0000118893,0.000006426373,0.00000380347,0.0001329544,0.000005097012,1.46309e-7,0.0002152682,0.8593404,0.129773,0.000008497039,0.00000698575,0.01049552],"study_design_scores_gemma":[0.0004968445,0.00007921726,0.0004060172,0.00005243577,0.0000243383,0.00001234731,0.0002566919,0.9545949,0.04385501,0.000001687126,0.00009170036,0.0001287754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.068234,0.00003286556,0.9295823,0.0000267135,0.001183318,0.0003624205,0.00005944143,0.0005044549,0.00001449769],"genre_scores_gemma":[0.9883005,0.00001709113,0.01143602,0.00000466276,0.00004969229,0.0001396339,0.00001508396,0.00002187726,0.00001540881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9200665,"threshold_uncertainty_score":0.6070606,"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."}}