{"id":"W2058393082","doi":"10.1115/ipc2004-0692","title":"Prediction of Corrosion Defect Failure Pressure for Finite Length Defects","year":2004,"lang":"en","type":"article","venue":"2004 International Pipeline Conference, Volumes 1, 2, and 3","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pipeline transport; Finite element method; Pipeline (software); Corrosion; Structural engineering; Constant (computer programming); Materials science; Failure assessment; Reliability engineering; Computer science; Engineering; Mechanical engineering; Metallurgy","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":[0.00009324855,0.000144014,0.0002011031,0.0001193245,0.00005677466,0.00004155747,0.000107744,0.0001421395,0.00007694586],"category_scores_gemma":[0.0001494893,0.0001259401,0.0001142115,0.00007819664,0.0000925647,0.0001627986,0.00002656667,0.0001583114,0.000002676511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002388371,"about_ca_system_score_gemma":0.00002641319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008215213,"about_ca_topic_score_gemma":0.0001794088,"domain_scores_codex":[0.9992545,0.00001126541,0.0002651172,0.0001953623,0.0001490866,0.0001246279],"domain_scores_gemma":[0.9994638,0.00007456177,0.0000529586,0.00009943682,0.0002530564,0.00005617108],"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":[0.0006835295,0.0004610628,0.05017028,0.003510437,0.001966975,0.000009433118,0.004341899,0.6856834,0.06350251,0.08575029,0.01834016,0.08558001],"study_design_scores_gemma":[0.002360849,0.0002266968,0.009506243,0.0003782533,0.0004033038,0.00002385562,0.000365054,0.9287536,0.007655267,0.02021899,0.02972572,0.0003822178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7569301,0.002991679,0.2346927,0.0007415646,0.001266965,0.0005233882,0.0009500529,0.0002326301,0.001671002],"genre_scores_gemma":[0.9974763,0.0005019381,0.001163189,0.0000240715,0.0001577853,0.00001634398,0.0001393029,0.00001123134,0.0005098471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2430701,"threshold_uncertainty_score":0.5135687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494286339358451,"score_gpt":0.2216896418849251,"score_spread":0.2067467784913406,"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."}}