Counting crocodiles from the sky: monitoring the critically endangered gharial (<i>Gavialis gangeticus</i>) population with an unmanned aerial vehicle (UAV)
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 km2 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".