{"id":"W3098409340","doi":"10.5267/j.ijiec.2020.10.002","title":"Monitoring fuzzy linear quality profiles: A comparative study","year":2020,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Ambiguity; Vagueness; Statistic; Quality (philosophy); Computer science; Process (computing); Data mining; Product (mathematics); Statistical process control; Mathematics; Machine learning; Statistics; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001124878,0.0001655695,0.0004493094,0.0003075598,0.0000760616,0.0002314734,0.001025483,0.00006005836,0.00002495574],"category_scores_gemma":[0.01201927,0.0001424875,0.0001269328,0.0006571093,0.00003543746,0.0005854655,0.0001422447,0.0006415646,0.0000359585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001334841,"about_ca_system_score_gemma":0.0001684037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006827487,"about_ca_topic_score_gemma":3.285519e-7,"domain_scores_codex":[0.9957197,0.0001537366,0.001534438,0.0002486047,0.002180542,0.0001630201],"domain_scores_gemma":[0.9941707,0.002534798,0.000743976,0.0001145096,0.002178687,0.0002573815],"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":[0.0001490999,0.0001589104,0.01636686,0.000001957176,0.0001916732,0.00007271418,0.003271233,0.9677389,0.0004260202,0.000593546,0.0002012287,0.01082784],"study_design_scores_gemma":[0.02188329,0.004169026,0.1203533,0.001000672,0.0002768447,0.0002108387,0.05364922,0.7626703,0.009990367,0.01395686,0.009593034,0.002246239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4629006,0.0000419535,0.5326691,0.001036174,0.002986062,0.0001918642,0.00002869337,0.00004416411,0.0001014247],"genre_scores_gemma":[0.9785662,0.000001502161,0.01802629,0.00002256552,0.003352559,0.000004751474,0.000001428071,0.0000126353,0.00001201791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5156657,"threshold_uncertainty_score":0.9963029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3795396119972876,"score_gpt":0.4953395330617689,"score_spread":0.1157999210644813,"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."}}