{"id":"W1976912071","doi":"10.1115/ipc2004-0267","title":"A Statistical Model for the Prediction of SCC Formation Along a Pipeline","year":2004,"lang":"en","type":"article","venue":"2004 International Pipeline Conference, Volumes 1, 2, and 3","topic":"Corrosion Behavior and Inhibition","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline (software); Pipeline transport; Computer science; Regression analysis; Data mining; Cathodic protection; Engineering; Machine learning","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":[0.000256003,0.0001220956,0.0001458732,0.00008344538,0.0001332358,0.00008069293,0.0001624512,0.00006467506,0.0002426286],"category_scores_gemma":[0.0001349396,0.00009188017,0.00005385376,0.00005552942,0.0001310665,0.0003821452,0.00004791646,0.00007860837,0.00001261674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000531247,"about_ca_system_score_gemma":0.0001038013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001206446,"about_ca_topic_score_gemma":0.0002009982,"domain_scores_codex":[0.9988404,0.00001603839,0.0004660776,0.0002091115,0.000317118,0.0001512775],"domain_scores_gemma":[0.9990773,0.00006611093,0.0001581828,0.0001271937,0.0005160505,0.00005517311],"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":[0.001196897,0.0009963836,0.002191904,0.0002401762,0.00001737016,0.000004445811,0.002945224,0.03806103,0.7198218,0.1267534,0.04793194,0.05983943],"study_design_scores_gemma":[0.00153475,0.00009620951,0.0005504525,0.00008788644,0.00004934053,0.00001165715,0.000206354,0.9666162,0.02054316,0.008862258,0.001327522,0.0001142217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1110285,0.00006020771,0.8860613,0.0005427375,0.0005215427,0.0003119638,0.001123894,0.00003737981,0.000312593],"genre_scores_gemma":[0.9944559,0.00007787794,0.003887983,0.0001086292,0.0001792449,0.00006811554,0.0002939534,0.00000926537,0.000919011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9285552,"threshold_uncertainty_score":0.3746765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03434220442167773,"score_gpt":0.2788387848368998,"score_spread":0.2444965804152221,"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."}}