Chickadee Song Structure is Individually Distinctive Over Long Broadcast Distances
Bibliographic record
Abstract
Abstract The two-note fee-bee song of male black-capped chickadees functions during the dawn chorus, in part, as a sexual signal across large distances. How song structure might encode information about male quality, however, remains unclear. We studied the availability of cues to male social rank (a proxy indicator of male quality), within the acoustic structure of dawn chorus songs of male chickadees whose flock dominance status we determined the previous winter. We used analysis of variance and discriminant function analysis to demonstrate that five temporal, frequency or relative amplitude features of song can predict individual identity but not the category of social rank (dominant versus subordinate) to which individuals belong. After transmitting chickadee songs through the forest and re-recording them at four broadcast distances, we found that song structure continued to effectively predict singer identity by our statistical methods despite significant acoustic degradation for as long as songs remained audible (up to 80 m). In particular, the relative frequency interval between the two notes is both the most invariant between-male measure and among the most individually distinctive. We conclude the structure of dawn chorus songs could function across large distances to signal the identity of familiar singing males whose relative quality is known to the listener from other interactions (such as encounters within winter flocks).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".