{"id":"W2292983827","doi":"10.1007/s12652-016-0362-7","title":"Wireless sensor networks for leak detection in pipelines: a survey","year":2016,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Water Systems and Optimization","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University","funders":"King Fahd University of Petroleum and Minerals","keywords":"Pipeline transport; Computer science; Focus (optics); Pipeline (software); Wireless; Wireless sensor network; Leak; Transient (computer programming); Leak detection; SIGNAL (programming language); Computer security; Telecommunications; Environmental science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007074014,0.00009309837,0.0002067059,0.0001386455,0.00005169133,0.00004730643,0.00007353027,0.00005236728,0.000002476833],"category_scores_gemma":[0.00004122461,0.00006584996,0.00004485273,0.00009962784,0.00001378345,0.0001239359,0.00001365475,0.00008710942,8.19935e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005147349,"about_ca_system_score_gemma":0.000006123347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002430822,"about_ca_topic_score_gemma":0.0007680265,"domain_scores_codex":[0.9991111,0.00003887546,0.0005269328,0.00008392902,0.00008074429,0.0001584441],"domain_scores_gemma":[0.999405,0.0001842005,0.000145537,0.00005226278,0.0001740998,0.00003886262],"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":[0.0001349991,0.00003375673,0.005026946,0.00009429879,0.00004677232,0.000007662524,0.001085388,0.8657451,0.0030707,0.00009708144,0.0001427989,0.1245144],"study_design_scores_gemma":[0.0004641719,0.0001509931,0.003519627,0.0004801247,0.000009405408,0.00004216389,0.0001887434,0.9881971,0.006517376,0.00008594926,0.0001947124,0.000149637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3776656,0.0001267853,0.6216758,0.00000615774,0.0004338613,0.00007030028,5.971602e-7,0.00001278347,0.000008126574],"genre_scores_gemma":[0.9987107,0.0001530813,0.0007968433,0.000008897442,0.000283305,9.322769e-7,6.015475e-7,0.00001495784,0.00003067126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6210451,"threshold_uncertainty_score":0.2685283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02925847341406986,"score_gpt":0.2416787867675856,"score_spread":0.2124203133535157,"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."}}