{"id":"W4387419433","doi":"10.1016/j.mineng.2023.108431","title":"Evaluation of biosorbents as an alternative for mercury cyanide removal from aqueous solution","year":2023,"lang":"en","type":"article","venue":"Minerals Engineering","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universidade de São Paulo; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Mercury (programming language); Cyanide; Adsorption; Chemistry; Effluent; Environmental chemistry; Pollutant; Aqueous solution; Periphyton; Banana peel; Pulp and paper industry; Nuclear chemistry; Environmental engineering; Environmental science; Organic chemistry; Food science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004343151,0.0004201426,0.0003796653,0.0003350625,0.0003292082,0.00044114,0.0002558407,0.0004848949,0.0007304532],"category_scores_gemma":[0.0004070538,0.0001430258,0.0003956477,0.0002716229,0.000213127,0.0002661874,0.0002883053,0.0002434447,0.0002031304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003501386,"about_ca_system_score_gemma":0.000510228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001972484,"about_ca_topic_score_gemma":0.003178313,"domain_scores_codex":[0.9995708,0.00009540371,0.00002172519,0.00003905686,0.0002071899,0.0000658468],"domain_scores_gemma":[0.9998197,0.00003662529,0.0000215137,0.000009931762,0.00008601673,0.00002611346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002591352,0.00009689514,0.0002174854,0.0001182786,0.00001331943,0.00004131457,0.00002194169,0.0002463887,0.9968381,0.00005608962,0.00002416617,0.002066816],"study_design_scores_gemma":[0.00001578549,0.000786254,0.000877713,0.000004830436,0.00002013434,0.00003603771,0.00003821599,0.001332964,0.9963237,0.00001826609,0.0005402964,0.000005750931],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977125,0.0003901674,0.001063535,0.00003690955,0.00001520177,0.00003924866,0.00005499486,0.0000145195,0.0006729849],"genre_scores_gemma":[0.9951504,0.0005878998,0.002064653,0.00002640278,0.000008360652,0.00003617277,0.00009571818,0.000008509159,0.002021808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001972484,"threshold_uncertainty_score":0.003922045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03698078906311308,"score_gpt":0.286391480411176,"score_spread":0.2494106913480629,"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."}}