{"id":"W2901110646","doi":"10.1016/j.aca.2018.11.036","title":"Validation and inter-laboratory study of selective hydride generation for fast screening of inorganic arsenic in seafood","year":2018,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"H2020 Marie Skłodowska-Curie Actions; Ústav analytické chemie, Akademie Věd České Republiky; Horizon 2020 Framework Programme; Ministerstvo Školství, Mládeže a Tělovýchovy; European Commission","keywords":"Chemistry; Certified reference materials; Hydride; Arsine; Detection limit; Arsenic; Inductively coupled plasma mass spectrometry; Chromatography; Hydrogen peroxide; Extraction (chemistry); Nitric acid; Mass spectrometry; Environmental chemistry; Hydrogen; Inorganic chemistry; Catalysis; Biochemistry","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":[0.00475362,0.001108535,0.0006718241,0.000590371,0.001102369,0.000840919,0.001259836,0.001382739,0.000913444],"category_scores_gemma":[0.003462669,0.0005937549,0.0009456574,0.0003907463,0.001329842,0.0005210084,0.001358464,0.0008084467,0.0007013071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008152789,"about_ca_system_score_gemma":0.00154729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003245232,"about_ca_topic_score_gemma":0.004474325,"domain_scores_codex":[0.9957069,0.001526699,0.0001868143,0.0009832128,0.001353177,0.0002432569],"domain_scores_gemma":[0.9978518,0.000570925,0.0002074868,0.0004467747,0.0008054478,0.0001176398],"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":[0.001221729,0.0006364716,0.005570958,0.00009251064,0.0001431463,0.0000770204,0.0005541285,0.001190239,0.9819378,0.0002068327,0.0001781681,0.008190985],"study_design_scores_gemma":[0.00006319983,0.003837581,0.005434554,0.00001180155,0.0001320937,0.0001923157,0.0002338926,0.005107344,0.9833615,0.00009651344,0.001494175,0.00003493706],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9535819,0.0002647429,0.04403224,0.0001402029,0.00009186313,0.0004248162,0.0003683184,0.0002711695,0.00082486],"genre_scores_gemma":[0.9638922,0.0002424269,0.03227424,0.000156031,0.00002907998,0.0004027718,0.0006548736,0.0000806012,0.002267924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00475362,"threshold_uncertainty_score":0.02513987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01543378799971953,"score_gpt":0.2539633691840306,"score_spread":0.238529581184311,"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."}}