Nesting Common Eiders (<i>Somateria mollissima</i>) show little behavioral response to fixed-wing drone surveys
Bibliographic record
Abstract
Drones may be valuable in polar research because they can minimize researcher activity and overcome logistical, financial, and safety obstacles associated with wildlife research in polar regions. Because polar species may be particularly sensitive to disturbance and some research suggests behavioral responses to drones are species-specific, there is a need for focal species-specific disturbance assessments. We evaluated behavioral responses of nesting Common Eiders (Somateria mollissima (Linnaeus, 1758), n = 19 incubating females) to first, second, or in a few cases third exposure of fixed-wing drone surveys using nest cameras. We found no effect of drone flights (F[1,23] = 0, P = 1.0) or previous exposures (F[1,23] = 0.75, P = 0.397) on the probability of a daily recess event (bird leaves nests). Drone flights did not impact recess length (F[1,25] = 1.34, P = 0.26); however, Common Eiders with prior drone exposure took longer recess events (F[1,25] = 5.27, P = 0.03). We did not observe any overhead vigilance behaviors common in other species while the drone was in the air, which may reflect Common Eiders’ anti-predator strategies of reducing activity at nests in response to aerial predators. Surveying nesting Common Eider colonies with a fixed-wing drone did not result in biologically meaningful behavioral changes, providing a potential tool for research and monitoring this polar nesting species.
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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 | 0.000 | 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) | 0.002 | 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".