Stage of development affects dry weight mercury concentrations in bird eggs: Laboratory evidence and adjustment method
Bibliographic record
Abstract
Bird eggs are often used to monitor levels and trends of mercury (Hg) in the environment. Logistical issues in such studies sometimes require the use of eggs at different stages of development. Therefore, it is important to understand how embryonic age may affect egg Hg concentrations. Studies using dry weight Hg concentrations in eggs assume that dry mass (e.g., lipids) does not change through embryonic development and thus expressing concentrations on a dry weight basis adjusts for any changes that may occur in egg mass throughout development. That assumption is tested in the present study by injecting chicken eggs with Hg and measuring Hg concentrations in embryonic contents at different stages of artificial incubation. The results indicate that dry weight Hg concentrations in eggs at later stages of development are statistically greater than in undeveloped eggs. To address this issue, we developed a method of standardizing egg Hg concentrations for embryonic age based on egg contents density (egg contents mass/egg contents volume). A combined dataset (n = 225) from precocial and semi-precocial birds was used to demonstrate that egg contents density declines with increasing embryonic development. Based on this finding, we applied a formula to adjust egg dry weight Hg concentrations for stage of development: adjusted egg [Hg] = unadjusted [Hg] × (egg contents density). The application of this approach to adjust data from the chicken egg Hg injection study resulted in there being no statistically significant difference in dry weight Hg levels in eggs at different stages of embryonic development. The adjustment approach described in the present study is a straightforward method to account for stage of development when assessing Hg levels in avian eggs and should be widely applicable to studies of this nature. Environ Toxicol Chem 2018;37:1168-1174. © 2017 SETAC.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".