{"id":"W2170699457","doi":"10.1109/nafips.2006.365854","title":"Predictive Fuzzy Control of Paper Quality","year":2006,"lang":"en","type":"article","venue":"","topic":"Textile materials and evaluations","field":"Materials Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Pulp (tooth); Computer science; Monte Carlo method; Chip; Fuzzy logic; Brightness; Process engineering; Data mining; Artificial intelligence; Engineering; Mathematics; Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005527825,0.00005987999,0.0001551913,0.0000171529,0.00004384543,0.00002335264,0.00008746461,0.00003435099,0.006800844],"category_scores_gemma":[0.00005024172,0.00004497522,0.00003529132,0.00004180429,0.000068787,0.0001271642,0.00001869007,0.00001398082,0.0002144169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001264973,"about_ca_system_score_gemma":0.00003004853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001045966,"about_ca_topic_score_gemma":0.00009352886,"domain_scores_codex":[0.999138,0.0001108486,0.0003064079,0.0001290442,0.0001924096,0.0001232943],"domain_scores_gemma":[0.9995345,0.00007727057,0.00009606746,0.0001703666,0.00009998027,0.00002183744],"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":[0.00003011464,0.00005326835,0.0005969685,0.000008353743,0.000002238908,1.484602e-7,0.00003553143,0.0002976765,0.9662856,0.03136886,0.001262418,0.00005882101],"study_design_scores_gemma":[0.001358428,0.0001110351,0.3106979,0.00001173776,0.00003726378,0.000001433568,0.0001323972,0.0002068155,0.6532382,0.03185107,0.00217048,0.0001831456],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9447136,0.00002826208,0.00622793,0.00026317,0.0002660626,0.0001564684,0.0001507534,0.00006866635,0.04812509],"genre_scores_gemma":[0.9984986,7.741556e-7,0.0006082958,0.0001154439,0.0001029451,0.00001926969,0.000006258016,0.000004609331,0.0006438124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3130473,"threshold_uncertainty_score":0.9941071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890267728820873,"score_gpt":0.2865037530432375,"score_spread":0.2676010757550288,"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."}}