Geographic song variation and dawn singing behavior of the cerulean warbler (Setophaga cerulea)
Bibliographic record
Abstract
This study presents the results of research into the vocal behavior of the Cerulean Warbler, a small, migratory songbird with learned songs that breeds in the eastern U.S. and southern Canada and winters in northern South America. Specifically, I 1) assessed patterns of geographic variation in the species’ songs, as well as 2) characterized the unique period of singing that occurs prior to sunrise, known as “dawn song.” I found that Cerulean Warbler song structure within the species’ core breeding range, where I had high power to discriminate differences, was highly uniform in all of the acoustic variables measured. Songs were remarkably constrained in their acoustic features: all songs were composed of 2-4 sections and had similar durations and frequency bandwidths. I failed to find geographically structured singing, or “dialects.” The dawn singing behavior of paired male Cerulean Warblers was best explained by seasonality (Julian date), although the breeding stage of the pair’s nest, as well as weather (rain, wind, and temperature) also influenced certain aspects of dawn song. Early in the breeding season, males sang at high rates, for long durations, and their dawn song bouts ended after sunrise. By mid-season, many males stopped singing dawn song, but those that continued sung at slower rates, for shorter durations, and their dawn bouts ended well before sunrise.
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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".