{"id":"W2039544037","doi":"10.1021/ac9027033","title":"Multiple Spiking Species-Specific Isotope Dilution Analysis by Molecular Mass Spectrometry: Simultaneous Determination of Inorganic Mercury and Methylmercury in Fish Tissues","year":2010,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Ministerio de Ciencia e Innovación; Agence Nationale de la Recherche; Fundación Caja Castellón","keywords":"Chemistry; Methylmercury; Isotope dilution; Mercury (programming language); Mass spectrometry; Isotopomers; Certified reference materials; Derivatization; Chromatography; Isotope; Inductively coupled plasma mass spectrometry; Reagent; Analytical Chemistry (journal); Environmental chemistry; Detection limit; Bioaccumulation; Molecule","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":[0.0004841259,0.0006372772,0.00043561,0.0005412321,0.0002217919,0.0004599526,0.0005867528,0.0007039335,0.0005524247],"category_scores_gemma":[0.000605032,0.0002713915,0.0004136277,0.0003791069,0.0003776197,0.0003315443,0.0005149168,0.0004532505,0.0004378373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004459175,"about_ca_system_score_gemma":0.0004445244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008483172,"about_ca_topic_score_gemma":0.001952679,"domain_scores_codex":[0.9992577,0.00007233729,0.00003580631,0.0002385739,0.0003578274,0.00003759715],"domain_scores_gemma":[0.999781,0.00004500456,0.00004566904,0.00003258535,0.00007973228,0.00001608154],"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":[0.00004005188,0.00001089503,0.0007373693,0.0000295568,0.00001127339,0.00001928014,0.00001567688,0.0001090168,0.9914563,0.00003054897,0.00001846413,0.007521532],"study_design_scores_gemma":[0.000005201786,0.0001517538,0.002871979,0.000003371506,0.0000245039,0.0002051554,0.00001398893,0.002080911,0.9935058,0.00005869235,0.001065971,0.00001265529],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7891831,0.002700778,0.2021626,0.0001788229,0.00009916032,0.0002757776,0.0008744298,0.0009926885,0.003532694],"genre_scores_gemma":[0.7503625,0.001909565,0.239443,0.0002355773,0.00002901259,0.0002795898,0.0006625399,0.000136212,0.006942008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008483172,"threshold_uncertainty_score":0.0032354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007942296454290614,"score_gpt":0.2433696403305328,"score_spread":0.2354273438762422,"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."}}