Population trend and ecology of the most isolated deer in the world, Bawean deer (Axis kuhlii): conservation challenges
Bibliographic record
Abstract
The Bawean deer plays a vital role in its small and isolated ecosystem as a herbivore and effective seed disperser, as well as holds cultural importance to the local community. However, the ecology of this Critically Endangered deer is poorly studied. Using random encounter and occupancy modeling based on 29,350 camera trap days between 2017 and 2019, we aimed to provide population estimates, habitat preferences, and behavioral data for this species. The population was 120–277 mature individuals, much less than the number in 1978. The density of Bawean deer could be related to the type of forest and the predation by free-roaming dogs as well as other factors such as the increase of wild pigs on Bawean Island. According to the best occupancy model, the tall and community forests far from human settlements are the most suitable areas for this species. Bawean deer is mainly crepuscular with significant daytime activity. Our results point out free-roaming dogs as a major threat to the native mammal community on Bawean island. We suggest the Bawean deer be listed as Critically Endangered following criteria B1a,b (ii, iii, v) of IUCN. Therefore, effective law enforcement and an adequate conservation strategy, including free-roaming dog control, are required to reduce the impacts of both direct and indirect threats.
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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 | 0.001 |
| 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".