GROWTH RATE AND SHEDDING OF VIBRISSAE IN THE GRAY SEAL, <i>HALICHOERUS GRYPUS</i>: A CAUTIONARY NOTE FOR STABLE ISOTOPE DIET ANALYSIS
Bibliographic record
Abstract
Abstract Stable isotopes have become powerful tools for gathering information on food webs in marine ecosystems. The method is based on the concept that the ratio of Nitrogen‐14 to 15N (or Carbon‐12 to 13C) in the tissues of animals is directly related to the ratio found in their diet. Vibrissae provide a time series of stable isotope data as tissue is laid down sequentially over time. Here we examine the growth rate of 283 mystacial (muzzle) vibrissae of four gray seals, Halichoeruas grypus, over a five‐month period to investigate their applicability for stable isotope diet analysis. The individual vibrissae did not grow at a constant rate during the study, Fifty‐nine actively growing vibrissae were modeled to quantify the growth pattern using a three‐parameter von Bertalanffy curve, with the parameters estimated using non‐linear mixed‐effects models. This model incorporated the inherent serial correlation of these data. The growth rate was 0.024 cm/d (95% CI = 0.019–0.030), the asymptotic length differed significantly by location (F3,56=9.64, P < 0.001), but no significant trend was found with muzzle location (F3,56= 0.15, P= 0.93). The Δlength/Δtime between each measurement was calculated and most of these data fell at or near zero growth (median = 0.04 cm/d, range = 0–0.78). Individual vibrissae were shed asynchronously and without any seasonal growth trend. This has serious implications for researchers attempting to extrapolate diet data from vibrissae. Because the growth is neither continuous nor synchronous, it will be a challenge to accurately identify the dates when the isotopes were incorporated into the tissue.
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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".