Influence of localized artificial light on calling activity of Common Poorwill ( Phalaenoptilus nuttallii )
Bibliographic record
Abstract
The presence of localized artificial light at night (or ALAN) drastically changes the landscape for organisms by providing areas of darkness and light within an area that was once only dark. This change can influence the behaviors of organisms in many ways. One such behavior is bird song. Many studies have examined how ALAN influences diurnal bird song, but few have examined its influence on nocturnal birds. We examined the influence of lunar illumination and localized artificial light on the calling behavior of the nocturnal Common Poorwill (Phalaenoptilus nuttallii; hereafter poorwills). To do so, we erected artificial light stations in poorwill territories and then conducted point count surveys at these stations to count poorwill calls with the lights turned on and off. We hypothesized that artificial light would have a similar effect to lunar illumination, and we would see an increase in calling when the lights were on. However, the results we obtained were mixed. We found there was no significant effect of artificial light on calling rate and, as expected, a strong positive effect of moonlight. Surprisingly, however, there was a negative effect of the interaction between moonlight and artificial light, with birds calling less when artificial light was on during nights with high lunar illumination. One possible reason for this result is increased visibility leading to increased predation risk under high levels of ambient illumination.
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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 | 0.000 |
| 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) | 0.001 | 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".