{"id":"W4410069527","doi":"10.1002/cjce.25724","title":"Optimization and prediction of chromium (iv) removal from synthetic acid mine drainage using green adsorbents: A Box–Behnken design and adaptive neuro‐fuzzy inference approach","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of South Africa","keywords":"Box–Behnken design; Acid mine drainage; Chromium; Adaptive neuro fuzzy inference system; Fuzzy inference; Fuzzy logic; Fuzzy inference system; Adsorption; Inference; Response surface methodology; Computer science; Chemistry; Materials science; Artificial intelligence; Environmental chemistry; Machine learning; Fuzzy control system; Metallurgy; Organic chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008740066,0.0007592884,0.0008128626,0.0004828048,0.0003478993,0.0007088478,0.0004673118,0.001108882,0.0007089721],"category_scores_gemma":[0.0007812492,0.0005333491,0.0009617566,0.0002979366,0.0002802628,0.0002752416,0.0002754724,0.000464868,0.0001129502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006186742,"about_ca_system_score_gemma":0.0008145944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01120782,"about_ca_topic_score_gemma":0.007584567,"domain_scores_codex":[0.9997489,0.00006804903,0.00002144648,0.00005719356,0.00007153581,0.00003296642],"domain_scores_gemma":[0.9996796,0.0001850323,0.00003228665,0.000009456307,0.00008452631,0.000009218699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001495184,0.0001492416,0.00145404,0.0001823051,0.00004497704,0.00006115023,0.00004246446,0.9657493,0.01813202,0.0003043179,0.00008797347,0.01364266],"study_design_scores_gemma":[0.000006656892,0.00008482504,0.0003108333,0.000003360973,0.00001041868,0.000003255791,0.000007933032,0.9962527,0.003208645,0.0000444556,0.00006253109,0.000004415558],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5916699,0.0005698822,0.4035621,0.0001291543,0.00004591483,0.0001881736,0.0001180104,0.0002911723,0.003425743],"genre_scores_gemma":[0.971014,0.0001397044,0.02774473,0.00002016245,0.000003477569,0.0001468205,0.00004821471,0.000007236011,0.0008756752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01120782,"threshold_uncertainty_score":0.02228516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406462358765066,"score_gpt":0.1900588181629834,"score_spread":0.1759941945753328,"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."}}