Carbon, nitrogen, and sulfur diet–tissue discrimination in mouse tissues
Bibliographic record
Abstract
Stable-isotope ratios are increasingly being used to examine ecological questions pertaining to dietary choices, physiological status, and animal migration. It has been shown that animal tissues reflect the isotopic signature present in food, altered by a small reproducible fractionation value. The average diet–tissue discrimination for δ 13 C and δ 15 N is approximately 1‰ and 3‰, respectively, although the degree of diet–tissue discrimination may be affected by a range of factors and vary between organisms and tissue types. Although the average δ 34 S is approximately zero, the sulfur diet–tissue discrimination values have only been determined for a few organisms. It is necessary to determine accurate diet–tissue discrimination values between tissue and dietary components to have confidence in any food-web study or determination of diet quality. In this paper, we determine carbon, nitrogen, and sulfur diet–tissue discrimination values for whole blood, liver, skeletal muscle, heart, brain, and fat (carbon only) from adult mice (Mus musculus L., 1758) equilibrated on three diets with unique isotopic signatures for carbohydrate carbon and for protein carbon, nitrogen, and sulfur. These data will aid researchers in choosing tissues to be used to examine food-web changes over time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".