Bibliographic record
Abstract
The function of nonvocal sounds in avian communication has rarely been investigated. I used playback of natural and digitally altered wing sounds from both sexes of Calliope Hummingbird (Stellula calliope) and Rufous Hummingbird (Selasphorus rufus) to test whether these stimuli altered their behavior. Stimuli played from behind target birds at artificial feeders were used to study responses that characterized the level of threat associated with each wing-sound stimulus. I classified the behaviors into one of five response categories and analyzed the data using log-linear (Poisson) models. I found that the wing-trill signal of males strongly altered the behavior of both conspecific and heterospecific recipients. Females responded more strongly than males to male stimuli, and stronger responses were elicited by male Rufous Hummingbird than by male Calliope Hummingbird stimuli. I used digitally altered wing-sound stimuli to investigate further which sound components are used in communication. The results, in conjunction with previous research that has shown fitness benefits to the sender of the signal, lead me to conclude that male wing trill is an important component of hummingbird communication. Female wing sounds elicited stronger responses than control sounds, but the base wing-hum sound itself appears to be a simple by-product of flight rather than a communication signal. These results of the first playback study using nonvocal avian sounds suggest that further research to identify which nonvocal sounds are communication signals is needed.
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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 | 0.000 | 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".