Individual acoustic differences in female black-capped chickadee (<i>Poecile atricapillus</i>) <i>fee-bee</i> songs
Bibliographic record
Abstract
In songbirds, song has traditionally been considered a vocalization mainly produced by males. However, recent research suggests that both sexes produce song. While the function and structure of male black-capped chickadee (Poecile atricapillus) fee-bee song have been well-studied, research on female song is comparatively limited. Past discrimination and playback studies have shown that male black-capped chickadees can discriminate between individual males via their fee-bee songs. Recently, we have shown that male and female black-capped chickadees can identify individual females via their fee-bee song even when presented with only the bee position of the song. Our results using discriminant function analyses (DFA) support that female songs are individually distinctive. We found that songs could be correctly classified to the individual (81%) and season (97%) based on several acoustic features including but not limited to bee-note duration and fee-note peak frequency. In addition, an artificial neural network was trained to identify individuals based on the selected DFA acoustic features and was able to achieve 90% accuracy by individual and 93% by season. While this study provides a quantitative description of the acoustic structure of female song, the perception and function of female song in this species requires further investigation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".