Assessing use of and reaction to unmanned aerial systems in gray and harbor seals during breeding and molt in the UK
Bibliographic record
Abstract
Wildlife biology applications of unmanned aerial systems (UAS) are extensive. Survey, identification, and measurement using UAS equipped with appropriate sensors can now be added to the suite of techniques available for monitoring animals – here we detail our experiences in using UAS to obtain detailed information from groups of seals, which can be difficult to observe from land. Trial flights to survey gray and harbor seals using a range of different platforms and imaging systems have been carried out with varying success at a number of sites in Scotland over the last two years. The best performing UAS system was determined by site, field situation, and the data required. Our systems routinely allow relative abundance, species, age–class, and individual identity to be obtained from images currently, with measures of body size also obtainable but open to refinement. However, the impacts of UAS on target species can also be variable and should be monitored closely. We found variable responses to UAS flights, possibly related to the animals’ experience of previous disturbance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".