Discrimination of carbon and nitrogen isotopes from milk to serum and vibrissae in Alaska Steller sea lions (Eumetopias jubatus)
Bibliographic record
Abstract
Knowledge of diet–tissue stable isotope discrimination is required to properly interpret stable isotope values and to identify possible diet shifts, such as might be expected from nursing through weaning. This study compared δ13C and δ15Ν οf paired serum and vibrissal roots with those of ingested milk (n = 52) from free-ranging Steller sea lion ( Eumetopias jubatus (Schreber, 1776)) pups (1–11 months) and juveniles (14–27 months) to estimate diet–tissue discrimination. Mean 15N enrichment from ingested milk to serum was 2.1‰ ± 0.6‰ and δ15Ν at the root of the vibrissae (representing current growth) were not significantly different from serum values. Milk was enriched for mean 13C by 5.0‰ ± 1.0‰ and 7.3‰ ± 1.2‰ relative to serum and vibrissal roots, respectively, which was due to the presence of 13C-depleted lipids in milk. This was confirmed by lipid extraction from a subset of milk and serum samples, resulting in a 5.8‰ ± 1.0‰ change only in milk. This study established that vibrissal roots and serum are reflective of a milk diet with approximately 2.0‰ 15N enrichment, and vibrissal roots reflect serum and lipid-extracted milk values with approximately 2.0‰ 13C enrichment. These discrimination factors are important to establish for stable isotope studies assessing diet shifts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".