The chemical forms of mercury and selenium in whale skeletal muscle
Bibliographic record
Abstract
Human exposure to potentially neurotoxic methylmercury species is a public-health concern for many populations worldwide. Both fish and whale are known to contain varying amounts of methylmercury species. However studies of populations that consume large quantities of fish or whale have provided no clear consensus as to the extent of the risk. The toxicological profile of an element depends strongly on its chemical form. We have used X-ray absorption spectroscopy to investigate the comparative chemical forms of mercury and selenium in fish and whale skeletal muscle. The predominant chemical form of mercury in whale is found to closely resemble that found in fish. In the samples of skeletal muscle studied, no involvement of selenium in coordination of mercury is indicated in either whale or fish, with no significant inorganic HgSe or HgS type phases being detected. The selenium speciation in fish and whale shows that similar chemical types are present in each, but in significantly different proportions. Our results suggest that for equal amounts of Hg in skeletal muscle, the direct detrimental effects arising from the mercury content from consuming skeletal muscle from whale and fish should be similar if the effects of interactions with other components in the meat are not considered.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".