Applications of Unmanned Aerial Vehicles to Survey Mesocarnivores
Why this work is in the frame
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Bibliographic record
Abstract
With the widespread extirpation of top predators over the past two centuries, mesocarnivores play an increasingly important role in structuring terrestrial trophic webs. However, mesocarnivores are difficult to survey at a population level because their widely spaced territories and nocturnal behavior result in low detection probability. Existing field survey techniques such as track plates and motion-sensitive camera traps are time-consuming and expensive, and yet still yield data prone to systematic errors. Unmanned Aerial Vehicles (UAVs) have recently emerged as a new tool for conducting population surveys on a wide variety of wildlife, eclipsing the efficiency and even accuracy of traditional methods. We used a UAV equipped with a thermal imaging camera to conduct nighttime mesocarnivore surveys in the prairie pothole region of southern Manitoba, Canada. This was part of a much larger ecological study evaluating how lethal removal of mesocarnivores affects duck nest success. Here, our objective was to describe methods and equipment that were successful in detecting mesocarnivores. We used a modified point-count survey from six waypoints that surveyed a spatial extent of 29.5 ha. We conducted a total of 200 flights over 53 survey nights during which we detected 32 mesocarnivores of eight different species. Given the large home ranges of mesocarnivores relative to the spatial and temporal scale of our spot sampling approach, results of these types of point-count surveys should be considered estimates of minimum abundance and not a population census. However, more frequent sampling and advanced statistics could be used to formally estimate population occupancy and abundance. UAV-mounted thermal imaging cameras appear to be an effective tool for conducting nocturnal population surveys on mesocarnivores at a moderate spatial scale.
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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 | 0.000 | 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) | 0.001 | 0.002 |
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