Long-term data indicates that supplementary food enhances the number of breeding pairs in a Cape Vulture<i>Gyps coprotheres</i>colony
Bibliographic record
Abstract
Summary The number of vultures is declining in many parts of the world due to numerous threats, such as poisoning and collisions with power-lines as well as the lack of adequate food sources. Vulture restaurants, i.e. supplementary feeding stations, have become a widespread conservation tool aimed at supporting vulture colonies. However, it is poorly understood how vulture restaurants influence population dynamics and whether they affect breeding success of vulture populations. We used a 12-year dataset from a breeding colony of the Cape VultureGyps coprotheresand a nearby vulture restaurant in South Africa to investigate the effect of supplementary food on population dynamics and breeding success. We found a significantly positive effect of supplementary food during the nest-building stage on the number of breeding pairs. However, breeding success, i.e. the proportion of successful nests, did not depend on supplementary food during the incubation and rearing stage. Especially during the critical rearing stage, the amount of food supplied might not have been sufficient to meet food demands of the colony. Still, our results indicate that carefully managed vulture restaurants might stabilise vulture colonies and can therefore aid vulture conservation.
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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) | 0.004 | 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".