Using GPS tracking for fruit bat conservation
Bibliographic record
Abstract
Abstract Understanding the ecology of species is key to the development of effective conservation measures. For many fruit bat species, however, even baseline knowledge of ecology and behaviour is lacking. To identify feeding sites of the Critically Endangered Livingstone's flying fox Pteropus livingstonii on the island of Anjouan, Comoros, we piloted the use of GPS loggers. Two bats (one female, one male) were tagged in early 2019, and data collected for 217 and 35 days, respectively. Acceleration data facilitated the classification of location points into behavioural categories. Potential feeding sites were located by cluster analysis of all location points that were attributed to a behavioural category in which feeding could occur. One important feeding site was located in an agricultural area. This is the first time quantitative behavioural data have been collected for Livingstone's flying foxes, providing insight into the ecological needs of this threatened species. These findings have the potential to inform applied conservation management decisions for protecting the resources required for the survival of this species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".