{"id":"W2121282743","doi":"10.1109/cca.1993.348280","title":"Adaptive basis weight control in paper machines","year":2002,"lang":"en","type":"article","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Basis (linear algebra); Computer science; Process (computing); Controller (irrigation); Adaptive control; Artificial intelligence; Weight control; Noise (video); Control theory (sociology); Machine learning; Control engineering; Control (management); Mathematics; Engineering","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":[0.0003432961,0.0003368237,0.0005099973,0.0002575576,0.0002387888,0.0007042519,0.0006363849,0.0005891763,0.001210767],"category_scores_gemma":[0.001042642,0.000202389,0.0002968501,0.000437993,0.0005431708,0.0007310643,0.0004085468,0.0006700261,0.0003403287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000417491,"about_ca_system_score_gemma":0.0003835785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003772282,"about_ca_topic_score_gemma":0.002555837,"domain_scores_codex":[0.9997701,0.0000511342,0.000008797191,0.00005172611,0.00009480881,0.00002350831],"domain_scores_gemma":[0.9997838,0.00007329719,0.00003983135,0.00002625636,0.00006491455,0.00001193962],"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":[0.0000592589,0.00003557265,0.0002817894,0.00006930142,0.00001944662,0.00006574261,0.00006738957,0.8681644,0.01087695,0.0302492,0.001025405,0.08908553],"study_design_scores_gemma":[0.000005136953,0.00002571285,0.0001109449,0.000002830777,0.00000198266,0.00001117352,0.00000308039,0.9932576,0.0008705563,0.004558032,0.001147717,0.000005096501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01606749,0.0004121843,0.9799569,0.0001159564,0.00006043398,0.00001792813,0.0000252285,0.0002763376,0.003067574],"genre_scores_gemma":[0.900854,0.0006732637,0.08762124,0.00007880289,0.00006713643,0.0001104482,0.00008378515,0.0000670131,0.01044434],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003772282,"threshold_uncertainty_score":0.007500648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00645261728533836,"score_gpt":0.1736296732566172,"score_spread":0.1671770559712788,"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."}}