Experimental variation in the spatial deposition of trace metals in feathers revealed using synchrotron X‐ray fluorescence
Bibliographic record
Abstract
Feathers can be used to investigate exposure to pollution in birds because they are a secondary route for the excretion of trace elements. Evidence based on analytical imaging and spectroscopy suggests that the spatial distribution of the essential trace element zinc within feathers is related to melanin pigmentation. However, our understanding of how trace elements are deposited into growing feathers is poor and has been hampered by a lack of analytical tools to examine the localization of trace elements within a feather. Here, synchrotron micro X‐ray fluorescence spectroscopy was used to map zinc directly within the barb and barbules of lesser scaup ( Aythya affinis ) feathers grown after experimental increases in dietary zinc. The results showed distinct spatial variation in zinc within barbs and barbules, with higher levels observed in the latter. Furthermore, increases in dietary zinc were found to increase the relative levels of zinc throughout the barbules from the base to the tip of the feather. Finally, analysis of feather cross sections revealed that regions of the feather barb and barbules with higher melanosome density also contained higher levels of zinc. These results provide a more detailed understanding of zinc and melanosome arrangement within the feather barb and barbules. Moreover, these results provide further support for the use of feathers as a noninvasive tool to study exposure to trace elements and highlight the utility of X‐ray spectroscopy in studies investigating impacts of a rapidly changing environment on wild bird health.
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 | 0.000 | 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".