{"id":"W2092360296","doi":"10.4043/25604-ms","title":"Real-time Arctic Pipeline Integrity and Leak Monitoring","year":2015,"lang":"en","type":"article","venue":"OTC Arctic Technology Conference","topic":"Offshore Engineering and Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Intecsea (Canada)","funders":"","keywords":"Pipeline transport; Warning system; Pipeline (software); Condition monitoring; Arctic; Schedule; Real-time computing; Environmental science; Computer science; Reliability engineering; 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":[0.0004220335,0.0005634516,0.0002957368,0.001043493,0.0002897119,0.0008659835,0.0004612472,0.0005479737,0.001491539],"category_scores_gemma":[0.001121533,0.0001487613,0.0002311005,0.0007089343,0.0002326684,0.0008659839,0.0006301056,0.0003371614,0.0006121247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005385464,"about_ca_system_score_gemma":0.0004098467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003973332,"about_ca_topic_score_gemma":0.003019335,"domain_scores_codex":[0.9993907,0.00007310233,0.00003233062,0.0001542911,0.0002917344,0.00005779132],"domain_scores_gemma":[0.9992638,0.0001029054,0.0001889665,0.00009311363,0.0003016784,0.00004948403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002257244,0.0003302864,0.06738839,0.0006485673,0.0001597715,0.001813689,0.0009103485,0.2282287,0.2994137,0.002877499,0.008975487,0.3869965],"study_design_scores_gemma":[0.00004375826,0.000575217,0.04052604,0.00008927384,0.00008042069,0.0009529492,0.0003995441,0.8032536,0.1360367,0.001836845,0.01609746,0.0001081536],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6440979,0.001531736,0.3277396,0.0005491224,0.000277525,0.0001318919,0.002579439,0.008035311,0.01505743],"genre_scores_gemma":[0.9810729,0.0002265883,0.01528551,0.00006080672,0.00003496395,0.00002057209,0.0006232462,0.00007240002,0.002603034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003973332,"threshold_uncertainty_score":0.007900417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02425438099568328,"score_gpt":0.2342092163090302,"score_spread":0.2099548353133469,"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."}}