When animals are not quite what they eat: diet digestibility influences<sup>13</sup>C-incorporation rates and apparent discrimination in a mixed-feeding herbivore
Bibliographic record
Abstract
The stable carbon isotope composition of animal tissues represents the weighted sum of the variety of food sources eaten. If sources differ in digestibility, tissues may overrepresent intake of more digestible items and faeces may overrepresent less digestible items. We tested this idea using whole blood and faeces of goats ( Capra hircus L., 1758) fed different food mixtures of C3lucerne ( Medicago sativa L.) and C4grass ( Themeda triandra Forssk.). Although blood and faecal δ13C values were broadly consistent with diet, results indicate mismatch between consumer and diet isotope compositions: both materials overrepresented the C3(lucerne) component of diets. Lucerne had lower fibre digestibility than T. triandra, which explains the results for faeces, whereas underrepresentation of dietary C4in blood is consistent with low protein content of the grass hay. A diet switch experiment revealed an important difference in13C-incorporation rates across diets, which were slower for grass than lucerne diets, and in fact equilibrium states were not reached for all diets. Although more research is needed to link digestive kinetics with isotope incorporation, these results provide evidence for nonlinear relationships between consumers and their diets, invoking concerns about the conceptual value of “discrimination factors” as the prime currency for contemporary isotope 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".