{"id":"W2080342349","doi":"10.1115/ipc2006-10327","title":"Advances in Feature Identification Using Tri-Axial MFL Sensor Technology","year":2006,"lang":"en","type":"article","venue":"Volume 2: Integrity Management; Poster Session; Student Paper Competition","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services","funders":"","keywords":"Magnetic flux leakage; Pipeline (software); Sizing; Identification (biology); Feature (linguistics); Computer science; Engineering; Mechanical engineering; Electromagnetic coil; Electrical engineering","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":[0.001318972,0.0003707141,0.0003602878,0.002309053,0.0002316544,0.0009919731,0.0007316453,0.000607437,0.001783842],"category_scores_gemma":[0.00260293,0.0002089945,0.0003047405,0.001098195,0.0005131424,0.002445366,0.0005418122,0.0005163368,0.001047039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005561154,"about_ca_system_score_gemma":0.0003212592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006644435,"about_ca_topic_score_gemma":0.001082952,"domain_scores_codex":[0.9984924,0.0002097858,0.00009931587,0.0002299139,0.0009096626,0.00005909352],"domain_scores_gemma":[0.9976609,0.0005669045,0.0004078468,0.0002720528,0.001037925,0.00005446112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002468574,0.0001484242,0.01286579,0.0004772495,0.00003234411,0.0001674955,0.0004616397,0.003694335,0.3219059,0.00636834,0.002279562,0.6513521],"study_design_scores_gemma":[0.00004032483,0.001507323,0.0312773,0.0001975483,0.0001156822,0.005347208,0.0005212981,0.1928818,0.6381195,0.006055697,0.1236836,0.0002526583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1341076,0.007118387,0.8317796,0.001374584,0.0002401908,0.0001350937,0.0002306507,0.002014875,0.02299913],"genre_scores_gemma":[0.5559655,0.003715876,0.4327966,0.0003419843,0.00018792,0.0000540946,0.0003118402,0.00009835245,0.006527787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002309053,"threshold_uncertainty_score":0.006975472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009050858221631201,"score_gpt":0.2647909547068562,"score_spread":0.2557400964852249,"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."}}