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Record W2096904355 · doi:10.1155/s1463924600000146

Development of a method for the determination of ultra‐trace level mercury in adipose tissue by cold vapour atomic fluorescence spectrometry

2000· article· en· W2096904355 on OpenAlexaboutno aff
Keith E. Levine, Reshan A. Fernando, Michelle Lang, Amal S. Essader, Robert W. Handy, Bradley J. Collins

Bibliographic record

VenueJournal of Analytical Methods in Chemistry · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersNational Institute of Environmental Health Sciences
KeywordsMercury (programming language)ChemistryDetection limitAdipose tissueCertified reference materialsPotassium bromateCold vapour atomic fluorescence spectroscopyBromideChlorideChromatographyAnalytical Chemistry (journal)Inorganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

A method for the determination of total mercury in rat adipose tissue by cold vapour atomic fluorescence spectrometry (CVAFS) has been developed. Adipose samples were initially subjected to a lyophilization procedure in order to facilitate the homogenization and accurate weighing of small tissue aliquots (approximately 50 mg). A closed vessel microwave digestion procedure using a mixture of sulphuric and nitric acids was used to liberate mercury from the adipose matrix. All mercury species were quantitatively oxidized to Hg(II) by a potassium bromate/bromide oxidation, then reduced to Hg(0) vapour by stannous chloride prior to fluorescence detection. The CVAFS exhibited a linear range of 10 pg Hg/ml to 120 pg Hg/ml. The method detection limit in solution was 2 pg Hg/ml, or 1 ng Hg/g adipose tissue, based on a nominal 50 mg sample and a final volume of 25 ml. A reference material from the National Research Council of Canada (DOLT-2, trace metals in dogfish liver) was prepared in quadruplicate in order to assess the accuracy and precision of the method. Mercury in this material was recovered at 2.22 +/- 0.08 microg/g, which is 104% of the certified level (2.14 +/- 0.10 microg/g).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.462
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.392
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2000
Admission routes1
Has abstractyes

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