{"id":"W1763231312","doi":"10.1109/ccece.1993.332225","title":"Quality prediction by neural network for pulp and paper processes","year":2002,"lang":"en","type":"article","venue":"","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Backpropagation; Artificial neural network; Artificial intelligence; Computer science; Kappa number; Machine learning; Pulp (tooth); Quality (philosophy); Data mining; Engineering; Pulp and paper industry; Kraft process","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006482277,0.00006765868,0.00007032132,0.000008999733,0.00007040319,0.00003946922,0.00002604498,0.00003808958,0.00006157383],"category_scores_gemma":[0.00002503649,0.00005731888,0.00001052887,0.0000689107,0.000009073731,0.0001654686,0.000004590605,0.00004559662,0.000002052305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006323923,"about_ca_system_score_gemma":9.737928e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005704457,"about_ca_topic_score_gemma":0.00001112666,"domain_scores_codex":[0.9996091,0.000004089774,0.0001059402,0.00009073825,0.00004510793,0.0001449895],"domain_scores_gemma":[0.9998553,0.00003904987,0.00001026193,0.00004218823,0.00001700756,0.00003617108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001601524,0.00004069826,0.01452499,0.002052109,0.0000516981,5.298346e-7,0.0006914262,0.08111478,0.00695972,0.0003515811,0.8104715,0.08372495],"study_design_scores_gemma":[0.000737576,0.00008588719,0.002698315,0.00006061615,0.00002301608,0.000009899183,0.0001000614,0.8824075,0.001153326,0.0008729873,0.1114291,0.0004217444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9470052,0.00862745,0.01929715,0.0005015524,0.0004901519,0.0003087792,0.00004795731,0.00121942,0.02250232],"genre_scores_gemma":[0.9967167,0.00008672012,0.0007431374,0.00008931853,0.0002097837,0.00002092492,0.00001038876,0.00001263346,0.002110439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8012927,"threshold_uncertainty_score":0.2337396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02498571299046335,"score_gpt":0.2374614788490068,"score_spread":0.2124757658585434,"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."}}