{"id":"W4380304018","doi":"10.1016/b978-0-323-99340-1.00005-8","title":"Wall thinning and damage detection techniques in pipelines","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Pipeline transport; Pipeline (software); Petroleum engineering; Forensic engineering; Engineering; Distortion (music); Identification (biology); Environmental science; Marine engineering; Mechanical engineering; Electrical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003522165,0.000454911,0.0004503295,0.0005348223,0.00005147672,0.00005304224,0.0001963082,0.0004522023,0.000008248112],"category_scores_gemma":[0.00005008806,0.0005000035,0.00007267902,0.00003614292,0.0001001663,0.00007586069,0.0001371144,0.0008415635,0.00002108988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001597616,"about_ca_system_score_gemma":0.00001709379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004698215,"about_ca_topic_score_gemma":0.0001480698,"domain_scores_codex":[0.9987162,0.00001722044,0.0004310402,0.0003814071,0.0001785622,0.0002755554],"domain_scores_gemma":[0.9993099,0.0001088344,0.00009178348,0.0003726692,0.00005614235,0.0000606992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003833513,0.000001264217,0.00005268378,0.0002920114,0.00002761193,0.0000653722,0.0001693117,0.000001179443,0.004286974,0.003645383,0.00003414369,0.9914202],"study_design_scores_gemma":[0.000214271,0.0001133762,0.0005164753,0.003937523,0.0000866849,0.00007855272,0.00001166932,0.0006359445,0.009292843,0.88056,0.1029765,0.001576164],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001063406,0.0002620293,0.0002168143,0.000009599868,0.0001766042,0.0005605668,0.000009136823,0.005001201,0.9927006],"genre_scores_gemma":[0.01744555,0.001040749,0.207903,0.0001508451,0.0008326595,0.0004711659,0.0000399923,0.001500763,0.7706153],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9898441,"threshold_uncertainty_score":0.9997451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01652054924489754,"score_gpt":0.2339591634958615,"score_spread":0.2174386142509639,"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."}}