{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005131087,0.001307267,0.001490107,0.002001323,0.0007710143,0.001707984,0.002303538,0.001742691,0.00355038],"category_scores_gemma":[0.01101635,0.0009700047,0.001678064,0.001402837,0.001156431,0.001525588,0.0008972265,0.002028606,0.001011883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001739902,"about_ca_system_score_gemma":0.001871289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03282542,"about_ca_topic_score_gemma":0.01313538,"domain_scores_codex":[0.9986221,0.0004210221,0.00008492613,0.0004624295,0.0002013418,0.0002082879],"domain_scores_gemma":[0.9898587,0.007750794,0.0009322683,0.0002539084,0.00106301,0.0001412283],"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":[0.00008586325,0.00004329474,0.006034077,0.00002745886,0.00004150689,0.00008456827,0.00004239657,0.9825682,0.0004581282,0.003257617,0.0003665679,0.006990393],"study_design_scores_gemma":[0.000003134615,0.0000166734,0.0003976289,0.000002395676,0.000005604734,0.000008489134,0.00000485394,0.9987515,0.00005306377,0.0007007301,0.00005123555,0.000004645912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2455695,0.0004658101,0.7468858,0.001042336,0.0001086454,0.000200814,0.001781824,0.001229449,0.002715759],"genre_scores_gemma":[0.9460889,0.000542422,0.04148316,0.0001355902,0.00009809015,0.0005363168,0.001827088,0.00009782381,0.009190657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03282542,"threshold_uncertainty_score":0.0652687,"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."}}