{"id":"W4200258693","doi":"10.22214/ijraset.2021.39572","title":"Development of KPI’s for Ageing Export Pipelines in the UK North Sea","year":2021,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Offshore Engineering and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Pacific Railway (Canada)","funders":"","keywords":"Pipeline transport; Integrity management; Submarine pipeline; Pipeline (software); Petroleum engineering; Environmental science; Forensic engineering; Engineering; Mechanical engineering; Environmental engineering; Geotechnical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003666507,0.0007957162,0.0003963133,0.006509504,0.0005822395,0.001861254,0.0005237672,0.0004667081,0.004755184],"category_scores_gemma":[0.009139721,0.0002633252,0.0005080255,0.004111807,0.0002960265,0.001459203,0.001257642,0.0006475312,0.001873352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004810969,"about_ca_system_score_gemma":0.002348774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0385612,"about_ca_topic_score_gemma":0.02393048,"domain_scores_codex":[0.9981698,0.0001802398,0.0002301091,0.0001586889,0.001077606,0.0001835404],"domain_scores_gemma":[0.9901012,0.0008634456,0.00207098,0.0002928449,0.006293871,0.0003776574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001253126,0.0005306787,0.5026999,0.002907595,0.0001473795,0.001581207,0.004049446,0.04032382,0.02561339,0.006345389,0.03684401,0.377704],"study_design_scores_gemma":[0.00007074198,0.001486481,0.8453307,0.0006412187,0.0001159175,0.0008514785,0.006377474,0.0543734,0.02543301,0.001470848,0.06368443,0.0001642325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8656805,0.00101954,0.03603195,0.001504227,0.0002093523,0.001602487,0.02539619,0.002380602,0.06617508],"genre_scores_gemma":[0.951395,0.0008370319,0.02550115,0.00004883733,0.00002175793,0.0003645939,0.01333796,0.00009159951,0.008402075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0385612,"threshold_uncertainty_score":0.07667351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06421220139410407,"score_gpt":0.3522875418926387,"score_spread":0.2880753404985346,"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."}}