Duty cycle, not signal structure, explains conspecific and heterospecific responses to the calls of Black-capped Chickadees (Poecileatricapillus)
Bibliographic record
Abstract
Animals can encode information into signals using at least 2 basic mechanisms. First, signalers can repeat their signals, encoding information into sequence-level parameters, such as signaling rate. Second, signalers can encode information into the fine structural variation of individual signals. This mechanism requires sophisticated encoding and decoding but potentially affords more rapid or efficient information transfer. The chick-a-dee call of Parid birds is a structurally complex signal that conveys food- and predator-related information to both conspecific and heterospecific receivers. However, the basic mechanism by which it communicates information is unclear. Previous research suggests that variation in the number of terminal notes is important, but this structural trait has not been manipulated independently from other structural traits or from sequence-level parameters, such as total duty cycle. We independently manipulated the fine structure and duty cycle of the calls of Black-capped Chickadees (Poecile atricapillus) and then broadcast them to potential receivers. Both conspecific and heterospecific receivers ignored manipulations to the fine structure of individual calls when the duty cycle of the signaling sequences was held constant. In marked contrast, receivers exhibited significantly stronger responses when the duty cycle was experimentally increased, and the fine structure of individual calls was held constant. Specifically, signaling sequences with a high duty cycle attracted more conspecific and heterospecific receivers and caused those receivers to approach the speaker more quickly, to approach the speaker more closely, and to remain within 10 m of the speaker for longer. These findings show that receivers respond to a simple sequence-level trait in a structurally complex avian signal.
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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 | 0.001 | 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".