Comparing occupied and unoccupied aircraft surveys of wildlife populations: Assessing the gray seal (<i>Halichoerus grypus</i>) breeding colony on Muskeget Island, USA
Bibliographic record
Abstract
Unoccupied aircraft systems (UAS) are now frequently used in wildlife research, including studies of marine species. Researchers are turning to UAS platforms for population assessment purposes because they may provide flexible, safe, and low-cost data collection. In these cases, it is important that the accuracy and precision of UAS-based approaches are evaluated to ensure data quality and comparability with legacy data. The present study compares image quality and survey performance of two small UAS with that of an occupied aircraft as applied to a population survey and molt-stage assessment of gray seals (Halichoerus grypus) in the northeastern United States. Population surveys using fixed-wing UAS and occupied aircraft provided similar quality imagery with only minor deviations in counts of both adult seals (<1% difference) and pups (3.7% difference). The multicopter UAS proved especially useful for molt-stage assessment when compared to both fixed-wing UAS and occupied aircraft surveys. The results of this study clearly illustrate that small UAS are reliable tools for conducting population assessments of pinnipeds and establishing life history stages of animals. These new tools provide flexibility in operations and may reduce costs and human risk in some cases.
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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 | 0.000 |
| 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) | 0.000 | 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 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".