Extensive browsing by a conventional grazer? Stable carbon isotope analysis reveals extraordinary dietary flexibility among Sanga cattle of North Central Namibia
Bibliographic record
Abstract
Intraspecies dietary flexibility, such as variable consumption of graze vs. browse in herbivores, has received scant attention on a spatial scale despite growing evidence of substantial variability within and among populations, especially in bovids. Here, we report on extraordinary differences in cattle diet among two communal pasture areas across seasons in northern Namibia: King Nehale (KN, open grassland) and Okongo (OK, dense woodland). Percentage C3 browse and C4 grass consumption was determined from δ(13)C values of dung samples, using a Bayesian stable-isotope mixing model (SIAR - stable isotope analysis in R). During the wet and early dry season, KN cattle consumed 11 and 19% browse, respectively, and the OK cattle consumed 84% browse. At the end of the dry season, the browse intake of KN cattle increased to 33% while that of OK cattle decreased to 55%. Vegetation structure influenced the graze/browse consumption strongly in both areas. A better understanding of this extraordinary dietary flexibility is imperative as anthropogenically driven habitat change is projected to lead to the extinction of perceived grazing specialists.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".