{"id":"W2045879391","doi":"10.1115/ipc2006-10595","title":"Monitoring and Prediction of Pipe Wrinkling Using Distributed Strain Sensors","year":2006,"lang":"en","type":"article","venue":"Volume 1: Project Management; Design and Construction; Environmental Issues; GIS/Database Development; Innovative Projects and Emerging Issues; Operations and Maintenance; Pipelining in Northern Environments; Standards and Regulations","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada); University of Alberta","funders":"","keywords":"Pipeline transport; Pipeline (software); Finite element method; Strain gauge; Structural engineering; Line (geometry); Computer science; Acoustics; Engineering; Geotechnical engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000474387,0.0004147536,0.0003826135,0.0003945541,0.0005864202,0.000136733,0.00006154907,0.0001242562,0.000009432861],"category_scores_gemma":[0.00001644685,0.0004097314,0.00002421003,0.0003781441,0.0004739588,0.0004480734,0.0001544974,0.0002528075,2.07033e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001673452,"about_ca_system_score_gemma":0.00002724128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002503357,"about_ca_topic_score_gemma":0.00003865284,"domain_scores_codex":[0.9979364,0.00006679873,0.0007138033,0.0005819883,0.0003136601,0.0003873773],"domain_scores_gemma":[0.9995828,0.00002796799,0.0001083357,0.0001761634,0.00003195673,0.00007275197],"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":[0.0002845594,0.0004384583,0.2905897,0.001786763,0.001385159,0.00008477324,0.01401603,0.413045,0.1153556,0.006155321,0.0001465937,0.156712],"study_design_scores_gemma":[0.006001903,0.0003463259,0.3678725,0.002124542,0.0003858562,0.0002872106,0.03486199,0.5566417,0.005990683,0.0007744426,0.02224417,0.00246873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7953165,0.002297877,0.2009604,0.00005127711,0.000106928,0.0007651873,0.0003735407,0.00007982954,0.00004850066],"genre_scores_gemma":[0.906267,0.004626829,0.08846963,0.000004657712,0.00005073128,0.00006988091,0.0002243764,0.0000459236,0.000240898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1542433,"threshold_uncertainty_score":0.9998354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01139149959782543,"score_gpt":0.226683376118132,"score_spread":0.2152918765203066,"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."}}