{"id":"W4404066072","doi":"10.1016/b978-0-443-15978-7.00110-7","title":"Enzyme digestion for speciation of arsenic","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Arsenic; Genetic algorithm; Digestion (alchemy); Biology; Chemistry; Evolutionary biology; Chromatography","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":[0.0002532527,0.001003244,0.0005419488,0.001008023,0.000341884,0.001166512,0.0006929655,0.000705045,0.04260797],"category_scores_gemma":[0.000174901,0.0004962878,0.0004279544,0.001313539,0.0002732104,0.001079769,0.0005120736,0.001331707,0.04743101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005037753,"about_ca_system_score_gemma":0.0004888666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001254749,"about_ca_topic_score_gemma":0.003732754,"domain_scores_codex":[0.9998848,0.000007224678,0.000006620273,0.0000260476,0.00006704322,0.000008255021],"domain_scores_gemma":[0.9999382,0.00002394666,0.000004029347,0.000008153285,0.00002100999,0.000004646994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008381083,0.000123519,0.0001795902,0.001113416,0.00002408197,0.0003255481,0.0001505954,0.0007661939,0.3356776,0.01497461,0.06593949,0.5806416],"study_design_scores_gemma":[0.000007010625,0.000063059,0.0004923301,0.0001390619,0.0000186426,0.0006861067,0.00005046274,0.000643249,0.116032,0.005298476,0.8765504,0.00001929177],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01096408,0.1138578,0.2283105,0.002325043,0.003326747,0.000211169,0.00249832,0.003384657,0.6351216],"genre_scores_gemma":[0.01094446,0.04576404,0.04756743,0.0004892959,0.0001867603,0.0000701125,0.001611927,0.0004705512,0.8928955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04260797,"threshold_uncertainty_score":0.1425378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114492761962312,"score_gpt":0.2261787500898462,"score_spread":0.214729473893615,"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."}}