{"id":"W4399864000","doi":"10.1007/s13399-024-05862-1","title":"Numerical parametric optimization with desirability functions for methylene blue dye removal by sunflower seed pericarp activated carbon","year":2024,"lang":"en","type":"article","venue":"Biomass Conversion and Biorefinery","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Methylene blue; Activated carbon; Sunflower; Parametric statistics; Sunflower seed; Chemistry; Carbon fibers; Pulp and paper industry; Environmental science; Horticulture; Organic chemistry; Mathematics; Adsorption; Engineering; Biology; Algorithm; Catalysis; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002174933,0.0002337754,0.0002023552,0.0001387559,0.0001679179,0.00008283422,0.00009180206,0.0001818123,0.0005873882],"category_scores_gemma":[0.00003582228,0.000163974,0.0001001263,0.001080594,0.0002607534,0.0001832496,0.00005936962,0.0001184253,0.0000380126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001879474,"about_ca_system_score_gemma":0.00002632689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001859845,"about_ca_topic_score_gemma":0.000003542348,"domain_scores_codex":[0.9985466,0.00006729995,0.0002288134,0.0005872284,0.0002943669,0.000275699],"domain_scores_gemma":[0.9994515,0.00009700203,0.00006662019,0.0001755463,0.0000225374,0.0001868123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000898606,0.000317902,0.009934719,0.0001053297,0.0001162212,0.00002622584,0.00009433313,0.0008559949,0.9696876,0.00003597355,0.00816021,0.009766931],"study_design_scores_gemma":[0.003189446,0.001530229,0.01226444,0.0000793929,0.0003422745,0.0002918142,0.0004654971,0.4935642,0.1836162,0.00002581509,0.3031354,0.001495265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978946,0.0001793593,0.01789858,0.001309403,0.0003648985,0.0004138911,0.00009007678,0.0002646376,0.0005331038],"genre_scores_gemma":[0.9941807,0.00004570231,0.003180801,0.0001688675,0.00004185235,0.0000172343,0.0001287855,0.00002389031,0.00221216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7860713,"threshold_uncertainty_score":0.6686667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01003322740599664,"score_gpt":0.2146179457582186,"score_spread":0.204584718352222,"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."}}