Evaluation of an unmanned aircraft system for detecting surrogate caribou targets in Labrador
Bibliographic record
Abstract
Regular, standardized population inventories have been suggested as an important component to the recovery of declining populations of boreal caribou (Rangifer tarandus caribou). Current survey methods typically employ manned aircraft, which can be noisy, expensive to operate, and dangerous for the people conducting the surveys. Small unmanned aerial systems (UAS) have garnered attention as a promising alterative to conducting aerial surveys in manned aircraft. Our research investigates the feasibility of using an UAS to conduct aerial surveys and determine which factors affect the detection of surrogate caribou targets, and hence may affect detection of real caribou. In the fall of 2013, we tested the capabilities of the Brican TD100E, a small, electric-powered fixed-wing UAS, to fly beyond visual line of sight near Goose Bay, Labrador. Seven surveys were done using different flight paths to collect aerial images of 26 surrogate caribou targets placed in six different habitats. Mixed effects logistic regression models were used to evaluate how habitat type, distance of the target from the image centerline, photo analysts’ experience level, flight time, and the target contrast against the landscape influenced the detection of surrogate caribou targets. We found that habitat type, target contrast, and the flight time affected target detection. Overall, 77.5% of the targets were detected; the odds of a photo analyst detecting a target in open habitat were roughly 10.5 times higher than in burned habitat and 42 times higher than in heavy forest. Target detection was influenced by the contrast of the target against the landscape, where a higher corrected integrated density was associated with greater target detection. The detection of targets was 87% during evening flights and 75% for morning flights. This study was the first of its kind to successfully fly a UAS beyond line of sight over land for non-military applications in North America and the findings of our research will provide an evaluation for using UAS to survey caribou in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".