Determination of mercury speciation in fish tissue with a direct mercury analyzer
Bibliographic record
Abstract
Knowledge of Hg speciation in tissue is valuable for assessing potential toxicological effects in fish. Direct Hg analyzers, which use thermal decomposition and atomic absorption spectrometry, have recently gained popularity for determining organic Hg after procedural solvent extraction from some environmental media, although quantitative recovery from lipid-rich materials, such as fish liver, has been problematic. The authors developed a new method by which organic Hg in fish liver and muscle is estimated by the difference between direct measurements of inorganic Hg in an acid extract and total Hg in whole tissue. The method was validated by analysis of a certified reference material (DOLT-4 dogfish liver) and naturally contaminated fish tissues with comparison to an established Hg speciation method (gas chromatography cold vapor atomic fluorescence spectrometry). Recovery of organic Hg from DOLT-4, estimated by difference, averaged 99 ± 5% of the mean certified value for methylmercury. In most liver samples and all muscle samples, estimates of organic Hg from the proposed method were indiscernible from direct speciation measurements of methylmercury (99% ± 6%). Estimation of organic Hg by the difference between total Hg and inorganic Hg was less accurate in liver samples with a high percentage of inorganic Hg (90%). This was because of the increased uncertainty that results from estimating a third value (i.e., organic Hg) by using the difference between two large concentrations (inorganic and total Hg). The proposed method is a useful tool for examining the speciation of Hg in fish muscle and liver, and by extension, potentially other tissues and environmental media.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".