Qiviut Trace and Macro Element Profile Reflects Muskox Population Trends
Bibliographic record
Abstract
ABSTRACT Understanding the drivers influencing ungulate population dynamics is crucial for developing conservation and management strategies to support wildlife health. Trace and macro elements are vital for ungulate growth, reproduction and survival. Thus, the trajectory of wildlife populations may be associated with element imbalances. Element concentrations can be measured in hair, an increasingly recognised bio‐monitoring tool. However, a better understanding of the relevance for wild ungulate population dynamics is needed. This study aimed to assess if element profiles in hair reflected the population trajectory of a keystone Arctic ungulate, muskox Ovibos moschatus, and whether benchmarks could be defined for element concentrations to assess population status. We measured qiviut (hair) element concentrations of 11 muskox populations ranging across northern America, including Greenland, and evaluated the association between element concentrations and different population trajectories. Seven trace and macro elements differentiated increasing populations from declining and stable populations using linear discriminant analysis. In general, copper, selenium, iron, manganese and cobalt tended to be at higher concentrations in increasing populations, whereas zinc and calcium were generally at lower concentrations in these populations, though variations were observed among populations. Benchmarks were defined for copper, selenium and iron, indicating populations were more likely to decline below a threshold concentration of these elements (‘limit’) and increase above a threshold concentration (‘target’). ‘Limit’ benchmarks were defined for zinc and calcium where populations were more likely to be increasing below this threshold value. Hair element profiles are a useful indicator of population trajectory in wild ungulate populations. Identified benchmarks can be used to assess population status, complementing ongoing but irregular and expensive monitoring efforts like population surveys, while trace element concentrations can provide insights into the mechanisms driving population change. Hair samples can easily be collected non‐invasively or alongside other monitoring activities, enhancing proactive wildlife management and conservation.
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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 | 0.001 |
| 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".