Acoustic signalling in Savannah Sparrows, Passerculus sandwichensis: Diel and seasonal variation, male-male vocal interactions, and responses to playback
Bibliographic record
Abstract
In this thesis, I examine the function of acoustic signals in Savannah Sparrows, Passerculus)sandwichensis, by analyzing temporal variation in vocal activity, and by conducting a playback experiment. In my first data chapter, I analyze longUterm acoustic recordings to study diel and seasonal variation in vocal activity of Savannah Sparrows. I show that singing activity of male Savannah Sparrows varies with time of day, time of year, and breeding stage. Males exhibit the highest level of song output in May, upon arrival on the breeding grounds, and the lowest level in August, before departure from the breeding grounds. Song output peaks in the early morning, consistent with dawn chorus behaviour, but this pattern is common only prior to pairing; after pairing the dawn chorus is reduced and male song output peaks in the evening, consistent with dusk chorus behaviour. These patterns suggest that dawn choruses serve a territorial function whereas dusk choruses serve a femaleUrelated function in Savannah Sparrows. In my second data chapter, I present the results of a playback experiment designed to test whether Savannah Sparrows signal their intention to attack a rival male. I simulated an intruder using song playback and a taxidermic model, and explored which behaviors were associated with physical attack. Savannah Sparrows produce soft songs and chip calls at significantly higher levels before attacking rivals, whereas three other measured behaviors (aggressive calls, wing waving, and passes over the model) do not predict attack. My research advances the field of animal communication and provides a foundation for future research on signal function and social interactions involving vocal signals.
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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".