Estimating Narwhal ( <scp> <i>Monodon monoceros</i> </scp> ) Age Using Epigenetic Analysis of Skin
Bibliographic record
Abstract
ABSTRACT Age is an important parameter to understand species life‐history characteristics. Recently, DNA methylation methods have emerged as innovative tools to estimate the age of marine mammals. Narwhal ( Monodon monoceros ) epigenetic age estimates were computed using beluga ( Delphinapterus leucas ), killer whale ( Orcinus orca ), bowhead ( Balaena mysticetus ), odontocete, cetacean, and mammalian epigenetic clocks and were compared with age estimates from counting growth layer groups in tusks ( n = 7) and/or aspartic acid racemization of eye lenses ( n = 25). Pearson's correlation coefficients ( R ) ranged 0.68–0.87 for the clocks, but the absolute accuracy of age estimates was quite low, with median absolute error (MAE) between epigenetic and reference age ranging 7–37.5 years. To address this lack of accuracy, we more broadly investigated DNA methylation levels at cytosine‐guanine sites (CpGs) for this same set of aged narwhals to develop a narwhal‐specific epigenetic clock. Using a penalized regression model on 1009 CpG sites, ten CpGs (plus intercept) were selected, resulting in a model with a R = 0.70 and MAE of 8.6 years. This is the first study to age narwhals using DNA methylation patterns and to develop a narwhal‐specific epigenetic aging clock, which will assist with understanding important life‐history characteristics for sustainable conservation 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".