Uneven Sampling and the Analysis of Vocal Performance Constraints
Bibliographic record
Abstract
Studies of trilled vocalizations provide a premiere illustration of how performance constraints shape the evolution of mating displays. In trill production, vocal tract mechanics impose a trade-off between syllable repetition rate and frequency bandwidth, with the trade-off most pronounced at higher values of both parameters. Available evidence suggests that trills that simultaneously maximize both traits are more threatening to males or more attractive to females, consistent with a history of sexual selection favoring high-performance trills. Here, we identify a sampling limitation that confounds the detection and description of performance trade-offs. We reassess 70 data sets (from 26 published studies) and show that sampling limitations afflict 63 of these to some degree. Traditional upper-bound regression, which does not control for sampling limitations, detects performance trade-offs in 33 data sets; yet when sampling limitations are controlled, performance trade-offs are detected in only 15. Sampling limitations therefore confound more than half of all performance trade-offs reported using the traditional method. An alternative method that circumvents this sampling limitation, which we explore here, is quantile regression. Our goal is not to question the presence of mechanical trade-offs on trill production but rather to reconsider how these trade-offs can be detected and characterized from acoustic data.
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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 | 0.028 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".