The influence of habitat on body size and tooth wear in Scottish red deer (<i>Cervus elaphus</i>)
Bibliographic record
Abstract
Body size has profound implications for ecology and life-history traits of mammalian species. Tooth wear is an indicator of food-processing investment and diet properties, with fitness consequences through differences in comminution efficiency, nutrient gain, and senescence. We investigate the relationships between mandible length (a proxy of skeletal body size), molar dentine thickness (a measure of tooth wear), and faecal neutral detergent fibre with residual ash (NDF–ash, a combined proxy of fibre and mineral components in the diet) in 874 male and female red deer (Cervus elaphus L., 1758) from 21 locations in moorland and woodland habitats across Scotland. Significant differences in mandible length occurred between habitats: woodland deer having larger mandibles than moorland deer. Within habitats, larger mandibles were related to higher rates of dentine wear, suggesting increased body size was associated with greater intake and processing of food. Both dentine wear and faecal NDF–ash were higher in moorland deer than in woodland deer, suggesting that fibre and (or) mineral abrasives in the diet may have contributed towards habitat differences in dentine wear. Between habitats, higher dentine wear was not associated with larger mandibles, in contrast to the relationship within habitats, indicating the precedence of additional environmental factors between habitats.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".