The effects of age on vocal mimicry in female superb lyrebirds
Bibliographic record
Abstract
Avian vocal mimicry has typically been examined through the lens of sexual selection acting on males. However, the females of many bird species are accomplished vocal mimics; a fact that cannot be accounted for by traditional male‐centric explanations for vocal mimicry. Female superb lyrebirds Menura novaehollandiae mimic primarily during nest defence, whilst male lyrebirds mimic predominantly during sexual advertisement. Here we examined the relationship between female age and vocal mimicry using a dataset of nesting female lyrebirds, several of which were recorded over multiple years. The vocal mimicry produced by females was diverse, and individuals varied greatly in how often they mimicked and what models they mimicked; however, neither the propensity to mimic nor the number of model sounds was explained by female age. Nevertheless, older females were more likely to mimic predators than younger females. There are two main implications of these findings. First, age is unlikely to explain intra‐population variation in female mimetic repertoires. Second, females might fine‐tune their mimetic repertoires as they age and mimic only models that are most effective during nest defence, such as predators. We discuss what these results mean for our understanding of vocal mimicry and vocal learning in songbirds of both sexes.
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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".