{"id":"W4405360477","doi":"10.1115/ipc2024-131698","title":"Pipeline Defect Detection Using Artificial Intelligence-Based Active Acoustic Sensing","year":2024,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline (software); Computer science; Acoustics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000374309,0.0002609321,0.0002711418,0.0002045919,0.0003349182,0.0006353116,0.00004658817,0.0001155671,0.00001629118],"category_scores_gemma":[0.0001558585,0.0002548688,0.0000402033,0.0001802608,0.00008628542,0.0003598625,0.00004028516,0.0001836626,0.000004446249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001448426,"about_ca_system_score_gemma":0.00004164519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002389002,"about_ca_topic_score_gemma":0.0000295597,"domain_scores_codex":[0.9987726,0.00005736093,0.0003948066,0.0003461515,0.0001130559,0.0003160885],"domain_scores_gemma":[0.9995812,0.00007487101,0.00003555652,0.0001348857,0.00009871071,0.00007483244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002476912,0.000006947826,0.0001127161,0.000252555,0.00003414035,0.00002406986,0.0003893368,0.005910039,0.9651397,0.0007386711,0.00001507631,0.02735196],"study_design_scores_gemma":[0.00008442642,0.00009962756,0.0004099069,0.001098785,0.0001021338,0.0001140758,0.0004482159,0.4156474,0.5761776,0.005275164,0.00008133962,0.0004613184],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6382928,0.0002866122,0.3591885,0.00001522023,0.001365104,0.000172159,0.00001835381,0.0005983324,0.0000628224],"genre_scores_gemma":[0.8813154,0.0001208946,0.1177431,0.000007570328,0.000716896,0.00001868605,0.000006929091,0.00005970333,0.00001077247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4097374,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02782754485219528,"score_gpt":0.266440600176072,"score_spread":0.2386130553238767,"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."}}