{"id":"W1978902941","doi":"10.1080/09349840802043471","title":"Optimization of Test Parameters for Magneto-Optic Imaging Using Taguchi's Parameter Design and Response-Model Approach","year":2008,"lang":"en","type":"article","venue":"Research in Nondestructive Evaluation","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research & Development Corporation","funders":"","keywords":"Taguchi methods; Fractional factorial design; Design of experiments; Orthogonal array; Sample (material); Eddy current; Set (abstract data type); Magneto; Factorial; Factorial experiment; Engineering; Computer science; Mechanical engineering; Statistics; Mathematics; Machine learning; Magnet","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":[0.006599323,0.0002207487,0.000291721,0.0009408136,0.000160934,0.00003956196,0.000190071,0.0001186509,0.000003525596],"category_scores_gemma":[0.004272435,0.0002465357,0.00004361374,0.0007793755,0.0004680023,0.0004993296,0.00006135683,0.0003495719,3.508112e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008009715,"about_ca_system_score_gemma":0.0003275248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003559986,"about_ca_topic_score_gemma":5.999305e-7,"domain_scores_codex":[0.9970058,0.0008543449,0.0004456587,0.0004448487,0.0007648876,0.0004845003],"domain_scores_gemma":[0.995144,0.003587272,0.00009682665,0.0003246774,0.0007640821,0.00008312269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002756832,0.00004486744,0.004415905,0.000114966,0.00001520103,0.000002447278,0.0005219543,0.9450353,0.04766606,0.0003391173,0.00001386516,0.001554591],"study_design_scores_gemma":[0.0007438459,0.0001711699,0.004224241,0.00009302793,0.00002214321,0.00006441199,0.0001074069,0.9129359,0.005100721,0.07633469,6.44997e-8,0.0002024451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5101538,0.00008546132,0.4880629,0.000007747401,0.00001875699,0.001447508,0.00000755413,0.00007474199,0.000141438],"genre_scores_gemma":[0.4822923,0.00001313534,0.5173918,0.000001301383,0.000007700615,0.0002482622,0.000007225605,0.00003753291,7.789874e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.07599556,"threshold_uncertainty_score":0.9999987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2393277670820059,"score_gpt":0.4010514965187009,"score_spread":0.161723729436695,"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."}}