How well do acoustic recordings characterize properties of bee (<i>Anthophila</i>) floral sonication vibrations?
Bibliographic record
Abstract
Floral sonication is a behaviour in which certain bees generate substrate-borne vibrations by contracting their flight muscles in order to extract pollen from poricidal anthers. Sonication vibrations, or ‘buzzes’, also contain a corresponding audible sound that results from vibrations radiating from the exoskeleton into the surrounding air. Acoustic recordings are often used as a proxy for analysis of floral sonication vibrations because they are more accessible to recording with conventional equipment such as microphones. However, the extent to which salient parameters of buzzes are reflected in the airborne components has not been experimentally verified. We examined correspondence in three properties (duration, frequency and amplitude) simultaneously recorded with acoustic and vibrational methods from freely foraging bumblebees. Duration and frequency are faithfully quantified from airborne recordings; however, two measures of acoustic amplitude (relative peak amplitude and sound pressure level) are not correlated with vibrational amplitude. Our findings validate acoustic recordings as a method to describe temporal and spectral components of floral sonication vibrations, but we caution against using acoustic measures of amplitude as proxies for the true vibrational power.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".