{"id":"W4410241975","doi":"10.18280/mmep.120431","title":"Performance Evaluation and Optimization of a Palm Kernel Cracker – A Taguchi-Grey Relational Analysis Approach","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Grey relational analysis; Taguchi methods; Palm; Kernel (algebra); Palm kernel; Computer science; Artificial intelligence; Mathematics; Statistics; Palm oil; Machine learning; Chemistry; Food science; Combinatorics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0002791547,0.0001201444,0.0002641628,0.0002367907,0.00005114431,0.00002671277,0.00004789314,0.00009381527,0.00006708047],"category_scores_gemma":[0.00007069458,0.0001088808,0.00005092909,0.0005882638,0.00003014506,0.00007386391,0.00002456383,0.0001106337,4.007954e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002466176,"about_ca_system_score_gemma":0.00001401964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004248227,"about_ca_topic_score_gemma":6.055622e-8,"domain_scores_codex":[0.9991909,0.000005439865,0.0002898674,0.0002025483,0.0001888504,0.0001224503],"domain_scores_gemma":[0.9995901,0.00009436096,0.00006258678,0.0001300046,0.00008472196,0.00003821695],"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":[0.000004438851,0.00004751662,0.001274254,0.0008227317,0.000281931,3.427431e-8,0.0001676965,0.9939578,0.0003041104,0.00300693,0.000001387139,0.0001311995],"study_design_scores_gemma":[0.0001984483,0.000005869093,0.0001133827,0.00009042472,0.0009874484,0.000001299876,0.00003052499,0.9965495,0.000654833,0.001265261,0.000002920325,0.0001000121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2415536,0.0004902236,0.7554272,0.00001516175,0.000004160344,0.00005322705,0.000001862024,0.00003276705,0.002421806],"genre_scores_gemma":[0.8958201,0.0001112531,0.1037587,0.000002728706,0.000006889369,0.00003056869,0.00002385849,0.000008242022,0.0002376446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6542665,"threshold_uncertainty_score":0.444003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02531988670629719,"score_gpt":0.2438229630686238,"score_spread":0.2185030763623266,"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."}}