Tissue–diet discrimination factors and turnover of stable carbon and nitrogen isotopes in white-footed mice (Peromyscus leucopus)
Bibliographic record
Abstract
Stable isotope analysis has become an increasingly valuable tool in investigating animal ecology. Here we document the turnover rates for carbon in the liver, muscle, and whole blood tissue, as well as the tissue–diet discrimination values for carbon and nitrogen isotopes in the liver, whole blood, muscle, and hair, of the white-footed mouse ( Peromyscus leucopus (Rafinesque, 1818)). A 168-day diet-switching experiment was conducted with a laboratory population of white-footed mice. The δ13C values for all tissues deviated less than 1‰ from those of the diet except for whole blood, which had a slightly higher tissue–diet discrimination factor of 1.8‰. All tissues were enriched in 15N by approximately 3‰ relative to the diet except for liver tissue, which was 4.5‰ higher than the dietary δ15N value. Turnover rates for tissues of white-footed mice were ranked liver > whole blood > muscle. The half-lives calculated for liver tissue differed significantly between the two diet switches performed in this experiment. We demonstrate that there is potential for variation in tissue–diet discrimination values and tissue turnover rates between even closely related species. These findings highlight the importance of determining species-specific estimates of these parameters prior to the use of stable isotope analysis in field investigations of animal ecology.
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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".