Impact of odorants on perception of sweetness by honey bees
Bibliographic record
Abstract
Organic volatiles produced by fruits can result in overestimation of sweetness by humans, but it is unknown if a comparable phenomenon occurs in other species. Honey bees collect nectar of varying sweetness at different flowering plants. Bees discriminate sugar concentration and generally prefer higher concentrations; they encounter floral volatiles as they collect nectar, suggesting that they, like humans, could be susceptible to sweetness enhancement by odorant. In this study, limonene, linalool, geraniol, and 6-methyl-5-hepten-2-ol were tested for their ability to alter behaviors related to perception of sweetness by honey bees. Honey bees were tested in the laboratory using proboscis extension response-based assays and in the field using feeder-based assays. In the laboratory assays, 6-methyl-5-hepten-2-ol and geraniol, but neither linalool nor limonene, significantly increased responses to low concentrations of sucrose compared with no odorant conditions in 15-day and 25-day-old adult worker honey bees, but not in 35-day-old bees. Limonene reduced responding in 15-day-old bees, but not in the older bees. There was no odorant-based difference in performance in field assays comparing geraniol and limonene with a no odorant control. The interaction of the tested plant volatiles with sucrose concentration revealed in laboratory testing is therefore unlikely to be a major determinant of nectar choice by honey bees foraging under natural conditions. Because geraniol is a component of honey bee Nasonov gland pheromone as well as a floral volatile, its impact on responses in the laboratory may reflect conveyance of different information than the other odorants tested.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".