{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004637853,0.001542395,0.001377442,0.001318603,0.0003973839,0.001104498,0.001131079,0.001046964,0.0009089304],"category_scores_gemma":[0.004178113,0.0006823735,0.00101641,0.0008664717,0.0005940135,0.0006781882,0.0004333903,0.0009402099,0.0003759597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009621457,"about_ca_system_score_gemma":0.001280853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006565399,"about_ca_topic_score_gemma":0.001595157,"domain_scores_codex":[0.9968414,0.001159363,0.0002690468,0.0002468406,0.001298231,0.0001850465],"domain_scores_gemma":[0.9977512,0.001321844,0.0003272988,0.0001476607,0.0004189974,0.00003293216],"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":[0.0006646275,0.0008809412,0.001660354,0.001518931,0.0001295763,0.0001108639,0.0002821879,0.2870605,0.5223781,0.003633589,0.0004202398,0.18126],"study_design_scores_gemma":[0.0001838098,0.004772307,0.00260263,0.00008149107,0.0002325959,0.0001692678,0.0001427321,0.5219529,0.4618754,0.0025388,0.005304908,0.0001432359],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04807013,0.0004075677,0.9492964,0.00004586094,0.00002548105,0.0006914254,0.00005856403,0.0002802743,0.001124389],"genre_scores_gemma":[0.2935223,0.0005133942,0.7029892,0.00004676561,0.00001002115,0.001924137,0.0001088264,0.00006596625,0.0008193942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004637853,"threshold_uncertainty_score":0.02452761,"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."}}