Age estimation of belugas, <i>Delphinapterus leucas</i>, using fatty acid composition: A promising method
Bibliographic record
Abstract
Abstract Data about age‐specific survival and mortality rate, as well as life history parameters are essential for studying population demography. However, noninvasive methods for ageing free‐ranging marine mammals are generally lacking. Recently, a few studies have highlighted the potential of using fatty acid (FA) composition in blubber biopsy samples to estimate age in some cetaceans. Here, we explore the opportunity of using this technique to estimate the age of free‐ranging belugas from three different populations. Belugas (Delphinapterus leucas) were sampled postmortem for blubber FA analysis and aged by counting the number of growth layer groups in teeth dentine. We found significant positive and negative relationships between some FAs and age. These relationships were stronger with outer blubber layer samples, the layer most accessible via biopsy, than with inner or middle layer samples, a pattern that is consistent with observed turnover rates and biological function across the blubber depth. The FA 12:0, 14:1n‐7, and 14:1n‐9 were promising correlates of age in belugas, allowing estimation of age with a precision of ±7–10 yr. Further work is required to determine the mechanisms underlying changes in FA composition with age and whether these mechanisms are stable through time and across populations.
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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 | 0.002 | 0.001 |
| 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".