Counting crocodiles from the sky: monitoring the critically endangered gharial (<i>Gavialis gangeticus</i>) population with an unmanned aerial vehicle (UAV)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Technology is rapidly changing the methods used in the field of wildlife monitoring. Unmanned aerial vehicles (UAV) are an example of a new technology that allows biologists to take to the air to monitor wildlife. A fixed-wing UAV was used to monitor the critically endangered gharial population along 46 km of the Babai River in Bardia National Park, Nepal. The UAV was flown at an altitude of 80 m along 12 pre-designed missions and, with a search effort of 2.72 h of flight time, acquired a total of 11 799 images covering an effective surface area of 8.2 km 2 of riverbank habitat. The images taken from the UAV could differentiate between gharial and muggers. A total count of 33 gharials and 31 muggers with observed density (per square kilometre) of 4.64 and 4.0 for gharial and mugger, respectively. Comparison of count data between one-time UAV and multiple conventional visual encounter rate surveys’ data showed no significant difference in the mean. Basking season and turbidity were important factors for monitoring crocodiles along the riverbank habitat. Efficacy of monitoring crocodiles by UAV at the given altitude can be replicated in high-priority areas with lower operating cost and acquisition of high-resolution data.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 it