{"id":"W2966589987","doi":"10.4995/ijpme.2019.10163","title":"An analysis of implementation of Taguchi method to improve production of pulp on hydrapulper milling","year":2019,"lang":"en","type":"article","venue":"International journal of production management and engineering","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Taguchi methods; Pulp (tooth); Orthogonal array; Mathematics; Design of experiments; Pulp and paper industry; Statistical analysis; Process engineering; Computer science; Statistics; Engineering; Dentistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001738718,0.0004956325,0.0006159839,0.0005106277,0.0002385877,0.0007058763,0.0005358944,0.0003315421,0.0007728895],"category_scores_gemma":[0.001594599,0.000241856,0.0005914788,0.0006098653,0.0002577286,0.0003741809,0.000233251,0.0004607902,0.0002154614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004083725,"about_ca_system_score_gemma":0.0004766531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007596326,"about_ca_topic_score_gemma":0.001505168,"domain_scores_codex":[0.9980049,0.0004306258,0.0001243061,0.0001715206,0.001180247,0.00008851339],"domain_scores_gemma":[0.9990159,0.0004114643,0.0001110122,0.00007352243,0.0003625316,0.00002558155],"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":[0.0009188899,0.0003449313,0.006307868,0.001098216,0.0001083753,0.0001883288,0.0003301196,0.01626159,0.8124879,0.0009876882,0.0004950244,0.1604711],"study_design_scores_gemma":[0.00004162879,0.003583351,0.01666883,0.00005462194,0.000200689,0.0001996528,0.0002124074,0.06263235,0.9113079,0.0003357187,0.004698171,0.00006480975],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6960276,0.002864226,0.2969731,0.0001339823,0.0001448456,0.0002231329,0.0001732403,0.0003350887,0.003124679],"genre_scores_gemma":[0.8671292,0.001119343,0.129033,0.00005084373,0.00001762375,0.000129948,0.000196406,0.00004033401,0.002283203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001738718,"threshold_uncertainty_score":0.009195328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01300413607086869,"score_gpt":0.3385418389721431,"score_spread":0.3255377029012744,"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."}}