Why birds sing at dawn: the role of consistent song transmission
Bibliographic record
Abstract
The dawn chorus is a widely observed phenomenon. One of the common, but inadequately studied, explanations for the occurrence of the dawn chorus is based on the rationale that atmospheric turbulence, which impairs acoustic communication, is least at dawn, and thus singing at dawn in some way maximizes signal performance. To investigate what possible acoustic benefit is gained through singing at dawn, we transmitted Swamp Sparrow Melospiza georgiana and White‐throated Sparrow Zonotrichia albicollis song through open grassland and closed forest both at dawn and at midday. The transmitted songs were re‐recorded at four distances from 25 to 100 m. Our results show that the mean overall absolute transmission quality of the signals was not significantly better at dawn than at midday. However, the signal transmission quality was significantly more consistent at dawn than at midday. Also, in general, signal transmission quality decreased with increasing distance. Variability in the transmission quality increased with distance for the White‐throated Sparrow song, but not for the Swamp Sparrow song. Consistency in signal transmission quality is a factor that, arguably, is crucial for the identity function of song. This study strongly supports the acoustic transmission hypothesis as an explanation for the existence of the dawn chorus while the demonstration of variability as a key factor in singing at dawn is novel.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".