Moonlight, but not cloud cover, predicts the vocal activity of Common Poorwills (<i>Phalaenoptilus nuttallii</i>)
Bibliographic record
Abstract
Daily and seasonal variation in light levels as well as meteorological variables can influence vocal behavior in both diurnal and nocturnal avian species. The Common Poorwill ( Phalaenoptilus nuttallii (Audubon, 1844)) is a visually orienting insectivore most active at dusk and dawn as well as during moonlit nights. Cloud cover is known to limit the amount of perceivable lunar light and generally creates a darker environment similar to when moonlight is low or absent. We examined the effect of cloud cover on the calling behaviour of the Common Poorwill while controlling for moonlight to test the prediction that lower light levels would reduce vocal activity. We conducted 291 point count surveys during the 2014 breeding season in southern British Columbia, Canada, starting at the end of civil twilight under various cloud and moonlight conditions. As expected, moonlight was a significant predictor of mean calling rate, but we found that neither cloud cover alone nor the interaction between moonlight and cloud cover was significant. Future studies should examine calling rates near urban centers to gain insight into the impacts of light pollution on nocturnal behaviours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".