{"id":"W2008577916","doi":"10.1115/ipc2004-0057","title":"Quantitative Evaluation of Indirect Inspection Reliability and Pipeline Reliability Based on Statistical Methods","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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Manitoba Hydro; Dynamic Systems Analysis (Canada)","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Pipeline (software); Corrosion; Interval (graph theory); Consistency (knowledge bases); Computer science; Pipeline transport; Forensic engineering; Engineering; Materials science; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001736897,0.0002454398,0.000405205,0.0002540902,0.00008876625,0.00005577528,0.0001437665,0.0001834893,0.0003395856],"category_scores_gemma":[0.0018941,0.0002135387,0.00009187885,0.0002468753,0.0003967761,0.0002034006,0.00003263151,0.0003790024,0.000006510905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002533723,"about_ca_system_score_gemma":0.0001401876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004994801,"about_ca_topic_score_gemma":0.0002393251,"domain_scores_codex":[0.9978931,0.0002728154,0.0006247615,0.0004504679,0.0005813908,0.0001774464],"domain_scores_gemma":[0.9980423,0.0004032196,0.0001157001,0.0002556856,0.001078409,0.0001046501],"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":[0.0007483962,0.000594528,0.008731297,0.0004499298,0.0002544998,0.000003559748,0.001185624,0.7839715,0.003294877,0.02285335,0.001177189,0.1767353],"study_design_scores_gemma":[0.001107386,0.0001543105,0.01844609,0.00009107645,0.0001456016,0.000003440482,0.0002270933,0.9429539,0.002663627,0.03368565,0.0003044891,0.0002172731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6151947,0.000329617,0.3779956,0.0007608933,0.0006596387,0.0003648385,0.0002386149,0.000155435,0.004300674],"genre_scores_gemma":[0.9653186,0.0001640588,0.03414862,0.00004704764,0.00007983977,0.00002216253,0.0001430475,0.00001409376,0.00006257704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3501239,"threshold_uncertainty_score":0.8707857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0361673203555519,"score_gpt":0.3399918087920051,"score_spread":0.3038244884364533,"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."}}