{"id":"W2899944452","doi":"10.1115/ipc2018-78364","title":"Pipeline Data Analytics: Enhanced Corrosion Growth Assessment Through Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petroleum Technology Alliance Canada","funders":"","keywords":"Computer science; Pipeline (software); Analytics; Data mining; Data analysis; Leverage (statistics); Reliability engineering; Engineering; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000227894,0.0001782124,0.0002505773,0.00005834181,0.0001487453,0.0000589325,0.0004356138,0.0001100671,0.001909038],"category_scores_gemma":[0.0001289489,0.0001333322,0.00006866319,0.0003528488,0.0001032598,0.0003401246,0.0001844262,0.0004243123,0.0001380587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006814616,"about_ca_system_score_gemma":0.00001326545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005307356,"about_ca_topic_score_gemma":0.0005417141,"domain_scores_codex":[0.9988723,0.00003909583,0.0003036543,0.0003248483,0.0002222831,0.0002377781],"domain_scores_gemma":[0.9990973,0.00008176069,0.00003516634,0.0005849247,0.0001404684,0.00006039518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002560168,0.0007942552,0.02833173,0.001480506,0.001862698,0.0000464447,0.004710765,0.1687268,0.5236487,0.02001332,0.1763777,0.07375106],"study_design_scores_gemma":[0.0002260849,0.00005652188,0.0003790606,0.00002234681,0.00009224272,0.000002887688,0.0001648351,0.9435272,0.04797443,0.0009648569,0.006370174,0.0002193627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0370216,0.0001812609,0.9015284,0.000428783,0.0006296893,0.0001198967,0.0000398733,0.0006423342,0.05940812],"genre_scores_gemma":[0.9774667,0.0003241189,0.01963528,0.0001168445,0.0002789396,0.00000183923,0.000300347,0.00001986143,0.001856037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9404451,"threshold_uncertainty_score":0.9990034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03327749440439507,"score_gpt":0.2973643822063148,"score_spread":0.2640868878019197,"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."}}