{"id":"W2330020580","doi":"10.1115/ipc2010-31646","title":"Statistical Predictive Modelling: A Methodology to Prioritize Site Selection for Near-Neutral pH Stress Corrosion Cracking","year":2010,"lang":"en","type":"article","venue":"2010 8th International Pipeline Conference, Volume 1","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stress corrosion cracking; Integrity management; Pipeline transport; Cracking; Corrosion; Reliability engineering; Probabilistic logic; Reliability (semiconductor); Computer science; Stress (linguistics); Pipeline (software); Ultimate tensile strength; Materials science; Welding; Statistical power; Structural engineering; Forensic engineering; Environmental science; Engineering; Metallurgy; Statistics; Composite material; Mechanical engineering; Artificial intelligence; Mathematics; Power (physics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000371288,0.0002564488,0.0003470719,0.0001899406,0.0001537579,0.0001912196,0.0003361665,0.0003108953,0.000888427],"category_scores_gemma":[0.0005715394,0.0002469389,0.0001401055,0.0001793748,0.0001258891,0.0002332518,0.00006267853,0.0008250729,0.00007224747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008389015,"about_ca_system_score_gemma":0.00008131386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006015506,"about_ca_topic_score_gemma":0.001436486,"domain_scores_codex":[0.9983839,0.00005897688,0.0004976825,0.0004381223,0.0002679435,0.0003534189],"domain_scores_gemma":[0.9981942,0.0003842702,0.000077604,0.0001849585,0.0009677652,0.0001912426],"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":[0.0006591916,0.0001541492,0.01220835,0.0001527858,0.0002392531,0.000004781135,0.001319499,0.8590159,0.07449571,0.02210486,0.01469452,0.01495095],"study_design_scores_gemma":[0.0003690167,0.00009373308,0.002008254,0.00003295435,0.00007535846,0.00001271976,0.00005690793,0.9768958,0.006166983,0.005749254,0.008273743,0.0002652372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2377189,0.000008973153,0.7571747,0.0004803548,0.003015085,0.0002959687,0.0006859889,0.0001690173,0.0004510867],"genre_scores_gemma":[0.8585142,0.00001320683,0.1384672,0.00009254097,0.0007061301,0.00006958218,0.0004135385,0.0000300486,0.001693442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6207954,"threshold_uncertainty_score":0.9999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03936628896850641,"score_gpt":0.2979304038519789,"score_spread":0.2585641148834725,"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."}}