Portable X‐Ray Fluorescence in the Assessment of Zinc Along Grizzly Bear Hair
Bibliographic record
Abstract
ABSTRACT As a biomarker of past uptake of zinc, hair presents an interesting option since its growth can provide a stored record over time. This study investigates the distribution of zinc in hairs gathered from a series of grizzly bears ( Ursus arctos horribilis ). A relatively new approach to measuring zinc along the length of a hair, portable X‐ray fluorescence (pXRF), was applied with 10 different bears each with a single strand of hair, and one bear chosen as a duplicate. Results obtained from pXRF were compared with zinc concentrations, using laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS) obtained from the same hair strands. Mercury was also measured by LA‐ICP‐MS to assess seasonal changes in diet associated with salmon consumption by some bears. Measurements started near the root end of the hair and, for the pXRF readings, progressed in 2 mm steps along the length of each hair. Energy spectrum analysis of the pXRF results provided a ratio of zinc to sulfur signal, which scaled with zinc concentration reported by LA‐ICP‐MS. Comparison of pXRF results with LA‐ICP‐MS zinc concentrations showed general agreement from individual hairs in both concentration levels and spatial trends. Nonetheless, some hairs revealed discrepancies between the pXRF and LA‐ICP‐MS outcomes. While it was assumed mercury and zinc would be correlated, and associated with salmon consumption in the fall, this was only the case for two bears, while the other bears had either negative relationships between the metals or no relationship at all. While mercury was consistently associated with salmon consumption in the fall, zinc was not. It appears that pXRF is a viable means of tracking zinc along the length of a single hair. This measurement technique has potential to be a rapid, non‐invasive approach to monitoring zinc in animal or human populations.
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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".