Golden monkey ranging in relation to spatial and temporal variation in food availability
Bibliographic record
Abstract
Abstract Understanding the determinants of a species’ range use aids in understanding their ecological requirements, which in turn facilitates designing effective conservation strategies. The ranging behaviour of golden monkeys (Cercopithecus mitis kandti) in Mgahinga Gorilla National Park, Uganda was studied from January 2003 to February 2004 to establish habitat preferences. In each 0.25 ha grid cell in the group’s home range we quantified the basal area of food trees (n = 12,133 trees), measured bamboo (Arundinaria alpina) stems (n = 103,548), and estimated vine and shrub coverage. The evaluation of habitat preferences was facilitated by the fact that only five plant species, plus invertebrates (7.5%) constituted 96.4% of the group’s foraging effort; this included bamboo (59.9%), Maesa lanceolata (18.7%), Hypericum revolutum (6.8%), Galiniera saxifraga (2.1%) and Ilex mitis (1.4%). Phenology data were collected for all five food tree species, three vines, and two shrubs. Range use generally followed food tree basal area distribution and not the distribution of bamboo, with the abundance of M. lanceolata being more closely associated with home range use than any other food plant. Bamboo was ubiquitous in distribution and a vital year‐round resource for golden monkeys, which they combined with other food items to meet their nutritional requirements. Illegal bamboo or tree extraction both pose a serious threat to the conservation of the golden monkey, but activities that affect food tree abundance will likely have the most influence on monkey persistence.
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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 | 0.002 |
| 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 | 0.001 | 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".