{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002295135,0.000331971,0.0002080279,0.0003648366,0.00008882857,0.0003337532,0.0004668472,0.0003056064,0.0006298444],"category_scores_gemma":[0.0006344818,0.000123463,0.0002018654,0.0002097215,0.0003094453,0.0004650681,0.0003566235,0.0002415276,0.0001345096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001975541,"about_ca_system_score_gemma":0.0001430348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005419682,"about_ca_topic_score_gemma":0.0005921221,"domain_scores_codex":[0.9998155,0.00003510947,0.000007363178,0.00003575711,0.00009509239,0.00001116368],"domain_scores_gemma":[0.9996915,0.0001511188,0.00005485928,0.00002273415,0.00006975065,0.0000101746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002548477,0.0002100496,0.001914221,0.0002242706,0.0000769942,0.0002038274,0.0001401085,0.3036143,0.4493822,0.004833601,0.0007903259,0.2383553],"study_design_scores_gemma":[0.000003778597,0.00006309601,0.000473982,0.000003400862,0.000006935224,0.00003482047,0.000006778564,0.978095,0.02029379,0.0006006194,0.0004113392,0.000006566721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1241762,0.0002642882,0.8709577,0.0001247508,0.00004786228,0.00003441044,0.00003229715,0.000673399,0.003689224],"genre_scores_gemma":[0.9194781,0.0001005133,0.07879454,0.00005407765,0.00001952461,0.00002763249,0.00002818413,0.00001783759,0.001479644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006298444,"threshold_uncertainty_score":0.002107024,"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."}}