Epigenetic Aging Clock for Long-Lived Fish Collected from the Wild
Bibliographic record
Abstract
Age information is fundamental in population biology. In fisheries management, robust and effective stock assessment models rely on fecundity and survival rates, and other life history traits that are generally age specific. Current aging methods for most fish species are based on the number of otolith growth rings, a time intensive method that requires lethal sampling and highly specialized expertise. To supplement current otolith-based aging efforts, here we develop a DNA methylation approach for aging Atlantic Halibut. We conducted whole-genome methylation sequencing on 66 wild caught individuals with otolith-derived age estimates. The resulting 14,588 CpG sites were evaluated as predictors of age in an elastic net model. We found a strong positive linear correlation between otolith age and predictions using a subset of 87 CpG sites selected by the elastic net model that had a mean absolute error of less than one year. The enzymatic treatment required for methylation sequencing with short-read technology like Illumina is still cost-prohibitive for routine application of large numbers of individuals. Accordingly, we conducted a successful pilot test to use adaptive nanopore sequencing for rapid, large-scale aging, and developed a framework to process the data for use in an aging framework. Our technique can be used to age Atlantic halibut when non-lethal sampling is needed (e.g., tagging studies) and to supplement otolith aging data for lethally sampled fish.
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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) | 0.002 | 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".