{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006424197,0.0005911444,0.0004028508,0.0006377118,0.0002504553,0.0005512205,0.0004876307,0.0007015888,0.001516193],"category_scores_gemma":[0.001934329,0.0002389927,0.0004087466,0.0007169538,0.0002251481,0.0007116053,0.0002245446,0.0006900007,0.0004051848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008482832,"about_ca_system_score_gemma":0.0004629412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01829728,"about_ca_topic_score_gemma":0.01246766,"domain_scores_codex":[0.9998152,0.00003370945,0.00001079937,0.00005218399,0.00006253443,0.00002556693],"domain_scores_gemma":[0.999505,0.0002662785,0.00005250741,0.00002299039,0.0001422903,0.00001089504],"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":[0.0001005302,0.00005516573,0.00238252,0.00003756068,0.00003129322,0.00003365871,0.00001586862,0.9271976,0.001855194,0.0005327929,0.0007152223,0.06704261],"study_design_scores_gemma":[0.000001298361,0.000004877622,0.0002890224,0.000001105773,0.000002271167,0.000001753728,7.825757e-7,0.9990215,0.0004049795,0.0002075539,0.00006346236,0.000001386469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3385653,0.001868333,0.6523308,0.000632656,0.0001450502,0.00005908313,0.0004390357,0.001789112,0.004170585],"genre_scores_gemma":[0.9387863,0.0006008752,0.05440534,0.00006800418,0.0000475662,0.00005700915,0.0005554302,0.00006647626,0.005413048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01829728,"threshold_uncertainty_score":0.03638154,"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."}}