Applications of Unmanned Aerial Vehicles for Conducting Mesocarnivore and Breeding Waterfowl Surveys in Southern Manitoba
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are becoming an increasingly important tool for wildlife surveys and equipping UAVs with thermal imaging cameras could make these surveys even more effective. In my thesis I examined the feasibility of using a UAV equipped with a thermal imaging camera to conduct mesocarnivore surveys, search for duck nests built over water, and conduct duck brood surveys in southern Manitoba.\nFor my first objective, I conducted nighttime mesocarnivore surveys with the UAV and thermal camera. I used a modified point-count survey from six waypoints and surveyed 29.5 ha in each replicate. I conducted a total of 200 flights over 53 survey nights during which I detected 32 mesocarnivores of eight different species. The UAV and thermal camera were effective at locating mesocarnivores, however given the large home ranges of mesocarnivores, my surveys should be considered estimates of minimum abundance and not a population census.\nFor my second objective I conducted a two-part survey: 1) I evaluated the effectiveness of a UAV and thermal camera to locate duck nests relative to traditional surveys, and 2) tested the hypothesis that technician visits to nests may influence predation rates. Over the course of my 1st study the UAV located a total of 47 nests that were not located by technicians, however, the technicians located 164 nests missed by the UAV, and both survey methods located 71 of the same nests. There was also no difference in survival rates for nests monitored with the UAV versus those monitored by technicians. Though the UAV completed surveys faster than technicians, the usefulness of this technology was limited, because the UAV has a relatively low detection rates.\nMy third objective was to evaluate the efficacy of using a UAV and thermal camera to conduct brood surveys. In 2018 and 2019, the UAV and thermal camera located a total of 1569 broods, compared to 666 located by ground technicians, and had higher detection rates (0.48 vs. 0.20). The UAV reliably located twice as many broods as ground technicians and completed surveys four times faster, indicating this technology has great utility for waterfowl biologists.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".