Source amplitude increases with body mass across avian genera
Bibliographic record
Abstract
Abstract Amplitude, or intensity, of sound is a fundamental characteristic of inter acoustic communication, with relevance in many scientific fields. The amplitude of an animal’s acoustic signal at its source (“source amplitude”) may be particularly relevant in the field of acoustic allometry, where relationships between species’ physical and acoustic features (e.g., dominant frequency) have been well-established across taxa. However, despite their potential scientific value, records and studies of source amplitudes remain remarkably scarce for avian species. Here we present novel estimates of source amplitude (range, mean, and median) for 17 species of Arctic-breeding birds, derived from measurements made in Utqiaġvik, Alaska during June 2024. We found a strong positive correlation between body mass and source amplitude in this data via a Markov Chain Monte Carlo multivariate generalized linear mixed model (MCMCglmm). Both phylogeny and individual identity were important random effects in this model. In contrast, random effects from environmental factors and measurement characteristics were minimal. Our work represents the first model describing the relationship between source amplitude and allometry across avian genera. We hope that this study will spur other investigations into avian source amplitude and its relationship to morphological and life history features for species in the Arctic and elsewhere.
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 | 0.002 |
| 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) | 0.002 | 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".