{"id":"W2216906076","doi":"10.1016/j.microc.2015.12.032","title":"Use of tartaric acid–citric acid–sucrose as chemical modifier for the determination of lead in several matrices employing ET AAS","year":2015,"lang":"en","type":"article","venue":"Microchemical Journal","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Council Canada; Fundação de Amparo à Pesquisa do Estado da Bahia; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Tartaric acid; Chemistry; Citric acid; Certified reference materials; Reagent; Detection limit; Effluent; Seawater; Spinach; Chromatography; Environmental chemistry; Environmental science; Environmental engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0004428694,0.0007787076,0.0002330863,0.000368476,0.0004514554,0.0002575702,0.0003921422,0.0004264395,0.0009470644],"category_scores_gemma":[0.000417799,0.0002733863,0.0003101708,0.0002599412,0.0003462841,0.0003299913,0.00024824,0.00060766,0.0004095233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002457717,"about_ca_system_score_gemma":0.0006701318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002206466,"about_ca_topic_score_gemma":0.006635814,"domain_scores_codex":[0.9997141,0.00005525912,0.00001977859,0.00007910755,0.00007957374,0.00005213643],"domain_scores_gemma":[0.9997764,0.00006390987,0.00002934399,0.00002900854,0.00007413336,0.0000271803],"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":[0.00004884104,0.000005589438,0.00008188686,0.00003495265,0.000006221997,0.00003348199,0.0000254882,0.00004389924,0.9982806,0.00005607654,0.00001429798,0.001368725],"study_design_scores_gemma":[0.000001152664,0.00008768135,0.0002668664,0.000001939829,0.00001141471,0.0000757388,0.00001294269,0.0002883803,0.9987804,0.00001803943,0.00045194,0.000003442906],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8992766,0.003814292,0.09038185,0.0002521457,0.0002373706,0.0001298069,0.000217536,0.0005012347,0.005189241],"genre_scores_gemma":[0.9390399,0.001867157,0.0539258,0.00007212451,0.00002281951,0.0000374217,0.0002046009,0.0000547001,0.004775416],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002206466,"threshold_uncertainty_score":0.0043872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09068824160071401,"score_gpt":0.342326104338579,"score_spread":0.2516378627378649,"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."}}