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Record W2046487377 · doi:10.1002/jccs.200000154

An Instrumental Correction for the Determination of Mercury in Biological and Sediment Samples Using Cold Vapor Atomic Absorption Spectrophotometry

2000· article· en· W2046487377 on OpenAlexaboutno aff
Meng‐Hsien Chen, Chiu‐Long Chou

Bibliographic record

VenueJournal of the Chinese Chemical Society · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryMercury (programming language)Atomic absorption spectroscopyCold vapour atomic fluorescence spectroscopyAnalytical Chemistry (journal)Environmental chemistryDetection limitCertified reference materialsSpectrophotometryChromatography

Abstract

fetched live from OpenAlex

Abstract Low recovery rate and inconsistent measurements were found in the determination of mercury by method of cold vapor atomic absorption spectrophotometry using the hydride formation system (Hitachi HFS‐2, Hitachi Ltd., Tokyo). To overcome this problem of insufficient reaction time we developed a simple T‐joint device attaching to the commercial HFS‐2 system for the determination of mercury in various biological tissues and sediment samples. The T‐joint device was designed to combine sample and reductant injection which increased the reaction time of the sample allowing a complete formation of mercury vapor and speeding up the analysis process in comparison to the traditional cold vapor atomic absorption spectrometric method. Recoveries of mercury were in the range 95% ‐ 100%. The corrected procedure gave precise and accurate readings with several certified reference materials: NIES No. 2 from the Japan Environment Agency; IAEA‐356 from the International Atomic Energy Association, and DOLT‐2, DORM‐2, TORT‐2, PACS‐1 and MESS‐2 from the National Research Council of Canada. Simple acid digestion methods were developed based on the sample Hg level and the nature of the sample. The sample detection limits were 0.0125 μg g −1 fresh weight and 0.0625 μg g −1 dry weight for biological samples, and as low as 0.0125 μg g −1 dry weight for sediment samples. These analytical protocols we established met the general requirements in environmental research and monitoring of mercury pollution.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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
GenreEmpirical

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

Citations6
Published2000
Admission routes1
Has abstractyes

Explore more

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