Designing epigenetic clocks for wildlife research
Bibliographic record
Abstract
The potential applications of epigenetic clocks are expanding in wildlife conservation and management. The pace at which they are being adopted highlights the need for field-specific design best practices. Epigenetic clocks were originally developed for human studies, presenting challenges for their adoption in wildlife research. Most notably, the estimated ages of sampled wildlife can be unreliable, and sampling restrictions limit the number and variety of available samples, which can reduce the accuracy of epigenetic clocks for wildlife. In this article, we present a detailed workflow for designing, validating, and applying wildlife epigenetic clocks in a way that accounts for sampling constraints. We provide recommendations for two main applications of wildlife epigenetic clocks: estimating unknown ages and assessing cumulative biological aging. Our simulations and analyses, applied to an extensive polar bear dataset from across the Canadian Arctic, demonstrate that accurate epigenetic clocks for wildlife can be constructed and validated with limited samples, accommodating projects with small budgets and sampling constraints. With our workflow and examples, we hope to make epigenetic clocks more accessible and widespread in wildlife conservation and management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".