{"id":"W2051968178","doi":"10.1115/ipc2014-33375","title":"External Pipeline Leak Detection Based on Fiber Optic Sensing for the Kinosis 12″–16″ and 16″–20″ Pipe-in-Pipe System","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Pipeline transport; Overheating (electricity); Leak detection; Leak; Optical fiber; SCADA; Pipeline (software); ALARM; Boiler (water heating); Petroleum engineering; Computer science; Marine engineering; Environmental science; Real-time computing; Automotive engineering; Engineering; Mechanical engineering; Electrical engineering; Waste management; Telecommunications; Environmental 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":[0.0002732673,0.0002329441,0.0002337229,0.0001236219,0.00009957842,0.00005095984,0.00008168245,0.000101888,0.00002329618],"category_scores_gemma":[0.00008720335,0.0001811089,0.00006764118,0.0001162906,0.00002919647,0.00008759221,0.00001575735,0.0001842451,0.00002602094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002617339,"about_ca_system_score_gemma":0.000004931527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005436623,"about_ca_topic_score_gemma":0.000465159,"domain_scores_codex":[0.9988939,0.00003576238,0.0002963766,0.0002738928,0.0001677541,0.0003323332],"domain_scores_gemma":[0.998705,0.0008085878,0.00004481134,0.0003318829,0.0000399128,0.00006979911],"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":[0.0001294864,0.00001894203,0.0001055755,0.0003702088,0.00002374648,0.000006478497,0.00009738486,0.8707484,0.02256555,0.0002492118,0.0001055505,0.1055795],"study_design_scores_gemma":[0.0008294162,0.00006519989,0.0002629231,0.0001689801,0.00003639636,0.00003278053,0.0001342792,0.9609367,0.03551491,0.00003965799,0.001755108,0.0002236344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08401345,0.0000580304,0.9103733,0.00007158784,0.0004281065,0.0004450104,0.000002976974,0.0003399503,0.004267554],"genre_scores_gemma":[0.9833322,0.00000452011,0.01537992,0.00008842657,0.0002205891,0.00001997376,0.000002147244,0.00007028897,0.0008819629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8993187,"threshold_uncertainty_score":0.7385405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009782989534793745,"score_gpt":0.2106170796382089,"score_spread":0.2008340901034151,"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."}}