Honeybee colony soundscapes: Decoding distance-based cues and environmental stressors
Bibliographic record
Abstract
Honey bees play a crucial role in agricultural productivity and ecological stability, yet their interactions with environmental stressors, particularly volatile organic compounds (VOCs) and pollutants, pose significant challenges to their cognitive functions and behavior. This study investigates the effects of VOCs on the acoustic communication within honeybee colonies and foraging behavior, specifically focusing on how these compounds influence distance-related cues conveyed through colony sounds. Using OpenL3 embeddings and machine learning models, the study achieved accurate classification of food source distances based on acoustic features, with the K-Nearest Neighbors (KNN) model demonstrating superior performance. The introduction of ethyl acetate and acetone caused minor reductions in classification accuracy but had divergent impacts on foraging dynamics: ethyl acetate enhanced landing efficiency, whereas acetone disrupted foraging activity. These findings highlight the utility of acoustic analysis for studying honey bee behavior and underscore the importance of mitigating environmental stressors to sustain pollinator populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".