Aerial Surveys Do Not Reliably Survey Boreal-nesting Shorebirds
Bibliographic record
Abstract
Aerial surveys have been used as a method for surveying boreal-nesting shorebirds, which breed in difficult-to-access terrain; however, the fraction of breeding birds observed from the air is unknown. We investigated rates of detection by conducting simultaneous air and ground surveys for shorebirds at three sites in the boreal forest of the Northwest Territories, Canada, in 2007. Helicopter surveys included both pond-based surveys where the helicopter flew around the perimeter of each wetland and transect-based surveys where observers recorded birds seen on line transects. Ground surveys involved intensive observation, territory mapping and nest searching in 5 km2 of plots over a period of 5-6 weeks. Shorebird densities observed from the helicopter were highest near large bodies of water. No shorebirds were observed over closed forest despite breeding densities on ground surveys being highest in closed forest. Detection rates were very low, varied among species and aerial survey types, and were inconsistent over time. Ground-based observations showed that the shorebirds often did not flush in response to the helicopter passing overhead. Owing to poor rates of detection, we conclude that helicopter surveys are not an appropriate method for surveying breeding shorebirds in boreal habitats, but may have some utility for monitoring birds' use of stop-over locations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".