Epigenetic age estimation in a long-lived, deepwater scorpionfish: insights into epigenetic clock development
Bibliographic record
Abstract
Age estimates are essential for fisheries assessment and management, but deepwater (>200 m) fishes are often difficult to age using traditional techniques. Therefore, age-predictive epigenetic clocks were developed for a model deepwater reef fish, blackbelly rosefish Helicolenus dactylopterus, using two tissue types (fin clips and muscle; n = 61 individuals; 9−60 years) and Δ 14 C-validated consensus age estimates. The influence of biological information (length and sex) on epigenetic clock accuracy, and the potential for developing a multi-tissue clock, were also assessed. Bisulfite-converted restriction site-associated DNA sequencing (bsRADseq) was used to identify CpG sites (cytosines followed by guanines) exhibiting age-correlated DNA methylation, and epigenetic clocks showed strong agreement ( R 2 > 0.98) between predicted and consensus ages. Including length and sex data enhanced accuracy and precision ( R 2 > 0.99; mean absolute error < 1 year). Age-associated CpG sites were identified across tissues, but a multi-tissue clock performed poorly relative to single-tissue clocks. Overall, results demonstrate that accurate and precise epigenetic clocks can be developed for deepwater fishes, and the inclusion of biological information may enhance clock accuracy and precision.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".