{"id":"W2010212697","doi":"10.1117/12.484105","title":"Quantitative interpretation of multifrequency eddy current data by using data fusion approaches","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Eddy current; Eddy-current testing; Sensor fusion; Fusion; Computer science; Fuse (electrical); Materials science; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001079633,0.0003743423,0.0004792701,0.0001325352,0.00006135594,0.00007277458,0.002113092,0.0001660899,0.000005728874],"category_scores_gemma":[0.001797168,0.0003444895,0.0002099306,0.0003993158,0.000280613,0.001334607,0.0004449823,0.0003980719,4.990635e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001889589,"about_ca_system_score_gemma":0.00004218058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001489767,"about_ca_topic_score_gemma":2.294551e-7,"domain_scores_codex":[0.9975942,6.580797e-8,0.0008817195,0.0005494089,0.0006231563,0.0003513867],"domain_scores_gemma":[0.9980011,0.0002399333,0.0004476934,0.0002438622,0.0009759007,0.00009150871],"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":[0.00004170279,0.0001286772,0.0004323754,0.001080992,0.000335778,3.783445e-8,0.00025105,0.0003041797,0.6853682,0.3092707,0.001957702,0.0008285977],"study_design_scores_gemma":[0.000736488,0.0002310827,0.0001256595,0.001034276,0.0002709821,0.00001607964,0.001189428,0.8462735,0.1390882,0.009952028,0.0005050182,0.0005772332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9901896,0.0006327456,0.00624261,0.00008148069,0.0002895743,0.0006250954,0.0005309287,0.0002078268,0.001200209],"genre_scores_gemma":[0.3189184,0.0001260291,0.6806625,0.000005062865,0.00006712705,0.00003623603,0.0001091059,0.00007249028,0.000002994616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8459693,"threshold_uncertainty_score":0.9999007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07000826378380416,"score_gpt":0.2950826414985092,"score_spread":0.2250743777147051,"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."}}