Using microphone arrays to examine effects of observers on birds during point count surveys
Bibliographic record
Abstract
ABSTRACT Point count surveys are widely used for monitoring songbird populations, but little is known of the effect of the observer on songbird behavior during point counts. We used a novel, wireless array of recorders to determine the location of singing birds with and without the presence of an observer. The array consisted of seven autonomous recording units synchronized to Global Positioning System (GPS) clocks, set around the perimeter of a 50-m-radius circle with one in the middle. Units were set to record automatically from half an hour before dawn until 10:00 each morning. We sampled 26 different locations in old fields at the Prince Edward Point National Wildlife Area in eastern Ontario between 1 June and 4 July 2007. Position estimates derived from a time-lag cross-correlation algorithm had a mean error of 1.7 m within 50 m and 5.6 m at 100 m from the center of the array. We found no difference in the positions of birds when an observer was present or absent. We also found no difference in the number of individuals or species detected or in the onset of singing. Our results suggest that, at least in the community we studied, observers conducting point counts do not cause significant changes in bird behavior.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".