{"id":"W2899778574","doi":"10.1115/ipc2018-78624","title":"Testing of an Oil-on-Water Sensing Technology for Detecting Pipeline Leaks in Remote Locations Subject to Freezing Conditions","year":2018,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Leak; Pipeline transport; Absorption (acoustics); Reliability (semiconductor); Computer science; Environmental science; Power (physics); Petroleum engineering; Electrical engineering; Engineering; Materials science; Environmental engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008025441,0.0001934588,0.0002917407,0.0002493325,0.0006039406,0.0001467968,0.0001369261,0.0001327479,0.00001122249],"category_scores_gemma":[0.0008221848,0.0001692059,0.00001605778,0.0002511811,0.0002891168,0.0002926397,0.0002316348,0.0001109647,0.000009753409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008845077,"about_ca_system_score_gemma":0.00001243572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001792811,"about_ca_topic_score_gemma":0.0002898491,"domain_scores_codex":[0.9984189,0.00005957346,0.0005161827,0.0004645488,0.0001139366,0.0004268254],"domain_scores_gemma":[0.9993894,0.00007487491,0.00008897422,0.0002962317,0.00008589854,0.00006460303],"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":[0.00002444629,0.00001961531,0.008765906,0.00003307178,0.000005190089,0.000002437521,0.001111068,0.0006651127,0.963312,0.0000701338,0.00002269329,0.02596836],"study_design_scores_gemma":[0.0004165672,0.000429588,0.007127869,0.000450305,0.00001414806,0.00002584198,0.001931897,0.003989866,0.9840978,0.00073625,0.0005014827,0.0002784095],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950497,0.00001278828,0.003459758,0.0004984631,0.000468261,0.0002572722,0.00002340291,0.0001573936,0.00007300269],"genre_scores_gemma":[0.8983279,0.00001049465,0.1011698,0.00002930559,0.0002646082,0.00003278132,0.000007159301,0.00002552477,0.0001323429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09771007,"threshold_uncertainty_score":0.6900017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03140998981913894,"score_gpt":0.2839562784979949,"score_spread":0.2525462886788559,"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."}}