Morphological characteristics of harbor seal (<i>Phoca vitulina</i>) whiskers and their use in dietary reconstruction using stable isotope ratios
Bibliographic record
Abstract
We investigated whether inferences about harbor seal (Phoca vitulina Linnaeus, 1758) whisker shed status and growth rates based on whisker morphology could improve dietary time series from whisker isotopic profiles. We identified morphometric characteristics of Alaskan harbor seal whiskers, including a smooth root section (SRS) and a bumpy section. The SRS was longer for spring versus fall-collected whiskers and matched the length of fully grown, shed whiskers. Thus, SRS length can differentiate whisker shed and growth status, and we used it to determine the sequence of whisker shedding by cohort in summer-captured harbor seals. The mean interbump length (IBL) correlated with whisker length, potentially providing a proxy for whisker growth rate. We compared carbon isotope ratios (expressed as δ13C values) along the three longest whiskers from 10 harbor seals, using two approaches for converting whisker position to date: (1) a standard growth rate constant based on captive studies and (2) individually adjusted growth rates based on whisker morphology. Intraindividual patterns of whisker δ13C values became more synchronous when expressed by deposition date rather than by position; however, adjusting growth rates based on IBL did not improve synchrony further. These findings suggest that whisker morphology can contribute whisker growth rate and shedding information for dietary reconstruction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".