{"id":"W2114807236","doi":"10.1109/papcon.1996.536003","title":"Neural network model for paper forming process","year":2002,"lang":"en","type":"article","venue":"","topic":"Material Properties and Processing","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Flocculation; Histogram; Turbulence; Planar; Filtration (mathematics); Suspension (topology); Materials science; Process (computing); Artificial neural network; Biological system; Drainage; Computer science; Algorithm; Mechanics; Physics; Engineering; Mathematics; Image (mathematics); Artificial intelligence; Chemical engineering; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003355076,0.0005717782,0.000592195,0.00035303,0.0003127458,0.0008497107,0.0009698439,0.001660195,0.005174675],"category_scores_gemma":[0.001146725,0.0002533785,0.000427158,0.000488677,0.0004603083,0.0007015176,0.0003613976,0.0008852758,0.0006274834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009658143,"about_ca_system_score_gemma":0.0005678989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01635173,"about_ca_topic_score_gemma":0.008227507,"domain_scores_codex":[0.9998438,0.00003974044,0.000007792743,0.0000475736,0.00003187438,0.00002922927],"domain_scores_gemma":[0.9997069,0.0001515586,0.00003116519,0.000009192579,0.00008715256,0.00001393788],"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":[0.00001996853,0.000008645496,0.000213491,0.00001881128,0.000009530873,0.00002959246,0.00001168224,0.9920779,0.0002753867,0.003990487,0.0002952099,0.003049368],"study_design_scores_gemma":[0.000002165065,0.000003772744,0.00004271556,0.000001136479,0.000001628834,0.000002741047,0.000001501483,0.9987784,0.00003544583,0.001003191,0.0001261042,0.000001227182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.10879,0.001786424,0.8582108,0.001203165,0.0002869602,0.00009036822,0.0008815874,0.0007466208,0.02800401],"genre_scores_gemma":[0.9431477,0.0008384125,0.02627209,0.0001290219,0.00007968052,0.0003765058,0.0005692267,0.00004157861,0.02854578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01635173,"threshold_uncertainty_score":0.03251308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0307249391788091,"score_gpt":0.2105632641509537,"score_spread":0.1798383249721446,"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."}}