{"id":"W2791182213","doi":"10.1007/s12652-018-0733-3","title":"A framework for single and multiple anomalies localization in pipelines","year":2018,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Water Systems and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University","funders":"King Fahd University of Petroleum and Minerals","keywords":"Pipeline transport; Pipeline (software); Computer science; Natural disaster; Risk analysis (engineering); Population; Computer security; Natural gas; Petroleum engineering; Environmental science; Business; Engineering; Geology","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":[0.001499666,0.0009973561,0.001295168,0.001567942,0.001069337,0.002091502,0.003449446,0.002021551,0.003352877],"category_scores_gemma":[0.003259477,0.000806411,0.001702882,0.001384094,0.001410581,0.002539101,0.003521412,0.002008158,0.0009417685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001222122,"about_ca_system_score_gemma":0.002476302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01908184,"about_ca_topic_score_gemma":0.01984586,"domain_scores_codex":[0.9990228,0.0001966443,0.00005352648,0.0002691587,0.0003179359,0.0001398713],"domain_scores_gemma":[0.9992067,0.0002339018,0.00007202411,0.000146354,0.0002654633,0.00007543039],"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":[0.0001021936,0.00008370604,0.0009792917,0.0001469852,0.0001042474,0.0002807634,0.0002440427,0.7033783,0.007637439,0.1747976,0.003620894,0.1086245],"study_design_scores_gemma":[0.000005585897,0.00001625931,0.00005958989,0.000006538084,0.00001098333,0.00003153792,0.00001970548,0.9793488,0.0006819643,0.01811235,0.001695125,0.00001153985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007903759,0.00004963871,0.9984747,0.00003822183,0.00001302797,0.00001054277,0.00002145642,0.0003079918,0.0002941185],"genre_scores_gemma":[0.1907576,0.0003662606,0.8040932,0.00007387545,0.0001023894,0.0001126639,0.0002307436,0.0002730013,0.003990291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01908184,"threshold_uncertainty_score":0.03794152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03885331528925131,"score_gpt":0.2633317275513249,"score_spread":0.2244784122620736,"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."}}