{"id":"W2053518421","doi":"10.1115/ipc2012-90201","title":"Identifying Initial Imperfection Patterns of Energy Pipes Using a 3D Laser Scanner","year":2012,"lang":"en","type":"article","venue":"","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada); University of Alberta","funders":"","keywords":"Scanner; Point cloud; Cylinder; Laser scanning; Reverse engineering; Focus (optics); Pipeline transport; Measure (data warehouse); Upgrade; Computer science; Mechanical engineering; Engineering; Laser; Optics; Artificial intelligence; Data mining; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001163434,0.0001177197,0.0001251014,0.0001341125,0.00003229151,0.00001583328,0.00006717454,0.00006472274,0.0001110316],"category_scores_gemma":[0.00002113212,0.0001181599,0.0000401699,0.000114164,0.00001793133,0.0003706999,0.00004100575,0.00007611512,0.000002622262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007879237,"about_ca_system_score_gemma":0.000006113112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006984248,"about_ca_topic_score_gemma":0.00003930146,"domain_scores_codex":[0.9993858,0.00001926515,0.0001760295,0.00008552964,0.0001039898,0.0002293991],"domain_scores_gemma":[0.9997039,0.00003641838,0.00003515374,0.0001364797,0.00003928067,0.00004870964],"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":[0.00001469613,0.00007365068,0.230973,0.0002930074,0.0001093195,0.000004789501,0.000486435,0.00046758,0.7476826,0.01008834,0.00016538,0.009641281],"study_design_scores_gemma":[0.0001912604,0.00003879816,0.01813944,0.0001539304,0.0000417145,0.0000649377,0.00006374701,0.004913456,0.9705988,0.005391394,0.00005544301,0.000347102],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7086498,0.00002911914,0.2858145,7.076767e-7,0.0002579141,0.00003276613,0.000002560068,0.0005082372,0.00470447],"genre_scores_gemma":[0.8409002,0.000004683658,0.1588606,0.000008609247,0.0001790147,0.000006382958,0.000002593392,0.00003312206,0.00000469835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2229162,"threshold_uncertainty_score":0.4818422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03153177716994029,"score_gpt":0.2814103319816835,"score_spread":0.2498785548117432,"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."}}