Designing Epigenetic Clocks for Wildlife Research
Bibliographic record
Abstract
The applications of epigenetic clocks - statistical models that predict an individual's age based on DNA methylation patterns - are expanding in wildlife conservation and management. This growing interest highlights the need for field-specific design best practices. Here, we provide recommendations for two main applications of wildlife epigenetic clocks: estimating the unknown ages of individuals and assessing their biological ageing rates. Epigenetic clocks were originally developed to measure biological ageing rates of human tissues, which presents challenges for their adoption in wildlife research. Most notably, the estimated chronological ages of sampled wildlife can be unreliable, and sampling restrictions limit the number and variety of tissues with which epigenetic clocks can be constructed, reducing their accuracy. To address these challenges, we present a detailed workflow for designing, validating applying accurate wildlife epigenetic clocks. Using simulations and analyses applied to an extensive polar bear dataset from across the Canadian Arctic, we demonstrate that accurate epigenetic clocks for wildlife can be constructed and validated using a limited number of samples, accommodating projects with small budgets and sampling constraints. The concerns we address are critical for clock design, whether researchers or third-party service providers perform the bioinformatics. With our workflow and examples, we hope to support the accessible and widespread use of epigenetic clocks in wildlife conservation and management.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".