{"id":"W4281727822","doi":"10.1109/tim.2022.3214606","title":"Digital Wire Analyzer of Mechanical Tension, Electrical Continuity, and Isolation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Office of Science; Syracuse University; University of Wisconsin-Madison; High Energy Physics; U.S. Department of Energy","keywords":"Spectrum analyzer; Voltage; Wire speed; Tension (geology); Acoustics; Vibrating wire; Electrical engineering; Network analyzer (electrical); SIGNAL (programming language); Materials science; Fundamental frequency; Resonance (particle physics); Measuring instrument; Electronic engineering; Engineering; Physics; Computer science; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001802824,0.00009840551,0.0001178683,0.0001441577,0.0001283908,0.00002345169,0.00003664523,0.00002733926,0.00003007984],"category_scores_gemma":[0.00000604379,0.0001070235,0.0000292874,0.0001728134,0.0000296663,0.000130798,0.000002040687,0.0001514233,4.823483e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001585277,"about_ca_system_score_gemma":0.00001579129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001000189,"about_ca_topic_score_gemma":0.000008808314,"domain_scores_codex":[0.9991705,0.00003209552,0.0002116684,0.0001398962,0.000348912,0.0000968692],"domain_scores_gemma":[0.9997419,0.00002283394,0.00003976895,0.00007788986,0.0000686308,0.00004900629],"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":[0.0002192033,0.0003633993,0.002915808,0.00009467026,0.0002405973,0.000003095383,0.0006550571,0.001797224,0.6677892,0.003462499,0.0002236869,0.3222355],"study_design_scores_gemma":[0.00925891,0.004729423,0.03769941,0.000285369,0.0006460648,0.0003828938,0.00216662,0.1454752,0.7573864,0.03903384,0.0009110972,0.002024761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5641378,0.00002923399,0.4344243,0.00005237779,0.0001538774,0.0002775657,0.00002628874,0.0002142842,0.0006843521],"genre_scores_gemma":[0.9921028,0.00002101428,0.007775123,0.00002490878,0.00000521137,0.00004905516,0.000003100016,0.00001367679,0.000005135731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.427965,"threshold_uncertainty_score":0.4364291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183139646162163,"score_gpt":0.2306943830073379,"score_spread":0.2088629865457163,"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."}}