Resource partitioning by grass height among grazing ungulates does not follow body size relation
Bibliographic record
Abstract
We compared the grass height grazed by white rhino, wildebeest, zebra and impala through the dry season months in the Hluhluwe–iMfolozi Park in South Africa. We expected that the grass height grazed would increase with the body size of the herbivore species, as suggested from past studies of resource partitioning among large mammalian herbivores. Instead we found that the largest of these species, white rhino, concentrated on the shortest grass, while the smallest species, impala, grazed heights intermediate between those grazed by wildebeest and zebra. Results suggest that the scaling of mouth width relative to body size, and hence to metabolic demands, may be the primary factor governing grass height selection, rather than body size alone. This calls into question the widespread assumption that smaller herbivores are superior competitors through being able to persist on sparser vegetation. Furthermore, there was considerable overlap in grass height grazed among these four species, indicating that niche separation by grass height is inadequate alone to explain their coexistence.
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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 | 0.000 | 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) | 0.002 | 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".