{"id":"W2081426401","doi":"10.1023/b:jone.0000022031.16580.5a","title":"Residual Magnetic Flux Leakage: A Possible Tool for Studying Pipeline Defects","year":2003,"lang":"en","type":"article","venue":"Journal of Nondestructive Evaluation","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Magnetic flux leakage; Residual; Materials science; Solid mechanics; Magnetic flux; Flux (metallurgy); Leakage (economics); Residual stress; Pipeline (software); Nondestructive testing; Detector; Magnetic field; Mechanics; Acoustics; Structural engineering; Composite material; Optics; Metallurgy; Engineering; Physics; Mechanical engineering; Computer science; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002652614,0.0003913719,0.0003072008,0.001199215,0.0001996212,0.0003545186,0.0004849991,0.0006312407,0.001205717],"category_scores_gemma":[0.0007542763,0.00016887,0.0001238657,0.0003272902,0.0005056334,0.0009793143,0.0002451106,0.0002910774,0.0002886896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001454877,"about_ca_system_score_gemma":0.0001292082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001845493,"about_ca_topic_score_gemma":0.0002967468,"domain_scores_codex":[0.9998472,0.00003156093,0.00000752645,0.00003533059,0.00005469897,0.00002365155],"domain_scores_gemma":[0.9992862,0.0002467249,0.0001830275,0.00009600678,0.0001494181,0.00003864963],"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":[0.0003825742,0.00006296461,0.006127759,0.0001866672,0.00001254465,0.0005582525,0.0001505863,0.0007424087,0.9593161,0.001056191,0.0001812396,0.03122269],"study_design_scores_gemma":[0.00002492683,0.001049821,0.01104639,0.00002885173,0.00006835345,0.002984772,0.0001760618,0.01011654,0.971078,0.0008154046,0.002587766,0.00002314928],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7932456,0.001876951,0.1983567,0.0001845003,0.00004700541,0.00009678146,0.0002957805,0.001428818,0.004467971],"genre_scores_gemma":[0.9821279,0.0002177277,0.01603427,0.00003691228,0.00001135334,0.0000169255,0.00006751771,0.00004206403,0.001445232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001205717,"threshold_uncertainty_score":0.004033506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04030962852073874,"score_gpt":0.3089354653831248,"score_spread":0.2686258368623861,"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."}}