{"id":"W4409627654","doi":"10.1016/j.compchemeng.2025.109143","title":"Predicting handsheet properties and enhancing refiner control using fiber analyzer data and latent variable modeling","year":2025,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Svenska Forskningsrådet Formas; Energimyndigheten; VINNOVA","keywords":"Latent variable; Spectrum analyzer; Computer science; Process engineering; Materials science; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001977684,0.0002427481,0.0003108536,0.000128069,0.00008605114,0.0001577267,0.0001879089,0.0001074759,0.00000278681],"category_scores_gemma":[0.00006692047,0.0002340569,0.00002110089,0.00019917,0.00002196123,0.0002814378,0.0002579463,0.0002830794,4.753416e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005927523,"about_ca_system_score_gemma":0.00001651151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002463539,"about_ca_topic_score_gemma":4.328533e-7,"domain_scores_codex":[0.9988571,0.000006703221,0.0003176501,0.0003676221,0.0001021086,0.000348849],"domain_scores_gemma":[0.9995271,0.00006444491,0.00002156218,0.0002578877,0.00003131433,0.00009767009],"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":[0.00000473927,0.000003881506,0.0001818679,0.0004548516,0.0000783254,0.000001996666,0.00007798748,0.7989857,0.198889,0.00001664662,0.00003333086,0.001271744],"study_design_scores_gemma":[0.0004309168,0.00000348294,0.000008702992,0.0009592349,0.0000698184,0.00001550342,0.00001097985,0.9928195,0.005306143,0.00001413507,0.0001202276,0.0002413053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6202638,0.002067027,0.3770396,0.00002198626,0.0001713579,0.00006985248,0.000005367753,0.0002846103,0.00007646936],"genre_scores_gemma":[0.9721497,0.00001992855,0.02757134,0.0000346378,0.0001443457,0.000004908365,0.00001173668,0.00003600401,0.00002744385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3518859,"threshold_uncertainty_score":0.9544563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056336428727162,"score_gpt":0.2034762604565895,"score_spread":0.1829128961693178,"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."}}