{"id":"W1963748562","doi":"10.1115/ipc2014-33263","title":"Towards Effective Pipeline Integrity Decision Making Under Uncertain Environment","year":2014,"lang":"en","type":"article","venue":"","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petroleum Technology Alliance Canada","funders":"","keywords":"Computer science; Integrity management; Probabilistic logic; Risk analysis (engineering); Data integrity; Reliability engineering; Process (computing); Prioritization; Rendering (computer graphics); Pipeline (software); Structural integrity; Reliability (semiconductor); Computer security; Engineering; Management science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007167595,0.001169182,0.001788663,0.001907274,0.001255746,0.00555365,0.002088819,0.00250834,0.00192897],"category_scores_gemma":[0.01358892,0.001072504,0.001508765,0.001812172,0.001665405,0.004771127,0.003467184,0.003079033,0.0003472021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002140277,"about_ca_system_score_gemma":0.004767535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00550929,"about_ca_topic_score_gemma":0.003963322,"domain_scores_codex":[0.995024,0.002226897,0.0003165347,0.0007755859,0.001217538,0.000439324],"domain_scores_gemma":[0.9931793,0.004345338,0.001069558,0.0002666143,0.000859177,0.0002798836],"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":[0.00008289562,0.00008455864,0.001077579,0.0002428641,0.0001108756,0.0002412737,0.0004369138,0.889398,0.002311079,0.06219002,0.0007954484,0.04302854],"study_design_scores_gemma":[0.00001414571,0.00004732035,0.0002362009,0.00006526803,0.0000318294,0.00003523324,0.0002240887,0.9177431,0.000950085,0.07919076,0.001436154,0.00002577277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01390554,0.0002315799,0.9822671,0.0008555095,0.00001929195,0.00008692333,0.00007150834,0.00008659502,0.002475903],"genre_scores_gemma":[0.4891523,0.0008164681,0.5076606,0.0002853267,0.0001126728,0.0002974986,0.0002259366,0.00004894145,0.001400328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007167595,"threshold_uncertainty_score":0.03790629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133542811882822,"score_gpt":0.2532140131556302,"score_spread":0.241878585036802,"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."}}