{"id":"W2558056647","doi":"10.4043/27372-ms","title":"Arctic Pipeline Integrity Management using Real-Time Condition Monitoring","year":2016,"lang":"en","type":"article","venue":"Arctic Technology Conference","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Integrity management; Reliability engineering; Condition monitoring; Probabilistic logic; Life extension; Pipeline (software); Computer science; Risk analysis (engineering); Risk management; Preventive maintenance; Warning system; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001417593,0.0002163329,0.0002798239,0.0003275237,0.00009732259,0.00002625043,0.0003291369,0.0003083783,0.0004315492],"category_scores_gemma":[0.00009705448,0.0001598361,0.00008270662,0.000433756,0.0002532679,0.0001788465,0.00009980326,0.0004063285,0.0001755309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004686613,"about_ca_system_score_gemma":0.00001494266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002459829,"about_ca_topic_score_gemma":0.00001584613,"domain_scores_codex":[0.9988316,0.00003003176,0.0003154294,0.0003224872,0.0001485724,0.0003519286],"domain_scores_gemma":[0.999167,0.00006321561,0.0000540433,0.0004821548,0.0001732386,0.00006037409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004451495,0.0001016838,0.03173734,0.0007201434,0.0008134923,0.0001092293,0.0002703424,0.0005363637,0.7701992,0.07249369,0.0001275453,0.1228464],"study_design_scores_gemma":[0.003028904,0.000264155,0.03295563,0.005272638,0.001266861,0.000406399,0.004122146,0.06595134,0.4587838,0.4236566,0.001596727,0.002694788],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733678,0.00007392556,0.02116827,0.00194881,0.0004073588,0.0001790496,0.000008126162,0.001084279,0.001762368],"genre_scores_gemma":[0.9934772,0.0004052664,0.005687024,0.000008036734,0.0000534794,0.00002166002,0.000002947803,0.00001859569,0.0003258035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3511629,"threshold_uncertainty_score":0.6517929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01991213780569746,"score_gpt":0.2586649981118525,"score_spread":0.238752860306155,"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."}}