Estimating diet composition for mountain hares in newly established native woodland: development and application of plant-wax faecal markers
Bibliographic record
Abstract
Knowledge of the feeding ecology of mammalian herbivores is fundamental in predicting their responses to habitat change. Where native woodlands are newly established in open moorland, the extent to which trees form part of the diet of mountain hares (Lepus timidus) is unknown. This information is necessary for predicting the potential effects of mountain hare browsing on woodland establishment. The n-alkanes and a long-chain fatty alcohols found in the cuticular wax of diet plants and faeces (N = 240) were used as markers to estimate the composition of the diet of mountain hares in an area of moorland with newly established Pinus sylvestris and Betula pubescens woodland. During winter, the diet of mountain hares was dominated by Calluna vulgaris, but there was a seasonal shift to a diet dominated by grasses, sedges, and rushes in summer. Pinus sylvestris and B. pubescens were minor dietary components in all seasons. A higher proportion of grasses, sedges, and rushes was found in the diet of lactating females. Results suggest that when an alternative browse species such as C. vulgaris is widely available, mountain hares may not have a large impact on the establishment of native woodland. The dietary results from this study are in broad agreement with those from previous studies using other techniques.
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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 | 0.000 | 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) | 0.001 | 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".