INDIVIDUAL VARIATION AND LEK-BASED VOCAL DISTINCTIVENESS IN SONGS OF THE SCREAMING PIHA (<i>LIPAUGUS VOCIFERANS</i>), A SUBOSCINE SONGBIRD
Bibliographic record
Abstract
One long-standing ornithological paradigm holds that song learning in oscine songbirds has a cultural component, whereas suboscine songbirds inherit songs genetically. Recent studies reveal that suboscine song may be more variable and complex than previously realized. Several suboscine species show marked individual variation in their songs—variation that may play a role in individual recognition and neighbor–stranger discrimination—and a few suboscine species show indications of song learning. We investigated individual variation in the vocalizations of a suboscine passerine, the Screaming Piha (Lipaugus vociferans), from recordings of 26 males at four lek sites along the Tambopata River in Peru. Male Screaming Piha songs consist of quiet introductory syllables followed by two explosively loud syllables that sound like an emphatic pee haw. We used three complementary methods to examine variation in song characteristics. Spectrogram cross-correlation revealed significant consistency within individual males and variability among males. Analysis of fine structural characteristics revealed that all measured song features were significantly less variable within individuals than among individuals. Canonical discriminant analysis based on these 13 song features correctly classified 93.2% of songs by individual and 76.4% of songs by lek site. Our results indicate that there is sufficient consistency in song features within males and sufficient variation among males for identification of individuals on the basis of songs and, to a lesser extent, that song features vary with the lek site of the singer. We conclude that Screaming Pihas sing songs that are individually distinctive and bear a lek signature.
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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 | 0.001 | 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".