Teeth as biomonitors of soft tissue mercury concentrations in beluga, <i>Delphinapterus leucas</i>
Bibliographic record
Abstract
Abstract This paper reports relationships between bulk Hg concentrations in the tooth cementum and soft tssues of fee-living beluga (Delphinapterus leucas). Total Hg levels were determined in slivers of cementum using a solid-sample Hg analyzer, a recent advance in Hg analysis that avoids acid predigestion. Tooth Hg concentrations ranged up to about 350 ng/g dry weight and were significantly correlated with Hg levels in kidneys, liver, muscle, and muktuk (skin) and with the age of the animals. The Hg/Se ratio in liver, the organ with the highest Hg concentrations, may have been an important determinant of tooth Hg. At hepatic Hg/Se molar ratios ≥0.6, tooth Hg increased steeply, suggesting that Hg in teeth may reflect physiologically available Hg that was not bound in the liver and that was circulating in the bloodstream. This Hg/Se ratio was exceeded in most beluga aged ≥20 years. The results indicate that teeth can be used as biomonitors to reconstruct temporal and geographic trends in the soft tissue Hg concentrations of beluga, provided that the age structures of the different populations are known.
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 | 0.001 | 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".