{"id":"W3089630458","doi":"10.1016/j.ijpvp.2020.104224","title":"External corrosion pitting depth prediction using Bayesian spectral analysis on bare oil and gas pipelines","year":2020,"lang":"en","type":"article","venue":"International Journal of Pressure Vessels and Piping","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Mitacs; BC Oil and Gas Research and Innovation Society","keywords":"Corrosion; Pipeline transport; Pitting corrosion; Bayesian probability; Environmental science; Materials science; Metallurgy; Statistics; Mathematics; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002329901,0.0004222943,0.0003105163,0.000611709,0.0001683801,0.0003002803,0.0004209059,0.0004511083,0.0004916429],"category_scores_gemma":[0.0009418614,0.0002260286,0.0003825963,0.0003541719,0.0002373543,0.0004578686,0.000265163,0.0003317483,0.0001680151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002398307,"about_ca_system_score_gemma":0.0002536306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006422699,"about_ca_topic_score_gemma":0.006844138,"domain_scores_codex":[0.9998841,0.00001903292,0.00000401,0.00003092912,0.00004009054,0.00002179568],"domain_scores_gemma":[0.9995609,0.0001578329,0.00005533486,0.00002842408,0.0001710263,0.0000265335],"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":[0.0006557737,0.0002063265,0.02680192,0.0001046027,0.00006568355,0.0002974124,0.0001123819,0.8236805,0.05124045,0.001003976,0.0006938174,0.0951372],"study_design_scores_gemma":[0.000001987837,0.0000132195,0.003253071,0.000001032796,0.000004376899,0.000008671973,0.000006016945,0.9952054,0.001369117,0.0001077428,0.00002560661,0.000003763613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8745228,0.00008244019,0.123877,0.00004594294,0.00001034058,0.00001009832,0.0001580767,0.0003125124,0.0009808365],"genre_scores_gemma":[0.9939896,0.00002264619,0.005598583,0.000003289414,0.0000035404,0.000002230031,0.00009680235,0.00001119653,0.0002721805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006422699,"threshold_uncertainty_score":0.01277065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531077788980835,"score_gpt":0.250420528702803,"score_spread":0.2351097508129947,"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."}}