The influence of food quantity on carbon and nitrogen stable isotope values in southern African spiny mice (<i>Acomys</i> <i>spinosissimus</i>)
Bibliographic record
Abstract
Stable isotope analysis is frequently applied as a tool to examine dietary patterns in animals. However, some of the underlying assumptions associated with using this approach are increasingly being questioned. We carried out a controlled diet experiment on the southern African spiny mouse (Acomys spinosissimus Peters, 1852) to test a number of aspects relating to these assumptions and also examine the hypothesis that stable isotopes, especially δ15N, can be used to provide evidence of nutritional stress. We compared the δ13C and δ15N values of livers and blood from animals that were fed ad libitum with animals undergoing a 10% reduction in food supply. Food-restricted animals showed no significant difference in δ15N; however, δ13C values of both liver and blood were depleted. Restricted animals also had a significantly lower C:N ratio. We examined the role of lipids and found following lipid extraction that both livers and lipids still showed the same separation in carbon values. Tissue–diet discrimination values were also calculated and found to be higher for both Δ13C and Δ15N compared with other mice species. Empirical values for discrimination rates were then compared with values calculated using an alternative method based on employing generic values and were found to be dissimilar, suggesting the use of generic values are not always appropriate. Our results highlight the need for greater understanding of the assumptions associated with using stable isotope analysis to examine diet and we suggest that studying a single species under captive conditions presents an ideal method to begin to test these hypotheses.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".