Honeybee vision and floral displays:from detection to close-up recognition
Bibliographic record
Abstract
In a social insect such as the honeybee, the survival of the colony depends on the success of its foragers. The bee optimizes its foraging success by returning to flowers of the species at which it has previously found food. This so-called flower constancy (see Chittka et al. 1999 for references) is based on the bee's capacity to learn and memorize specific flower signals (Menzel et al . 1993; Menzel & Müller 1996; Menzel 1999 and this volume) and to discriminate among different species by their different signals. A bee returning to the feeding site in search of a flower, be it natural or artificial, must first detect the target from a distance. Once the flower has been detected, the bee will approach it up to a distance at which it is able to recognize whether or not the flower is similar to that stored in memory. Among the different sensory cues used, visual cues are of fundamental importance. In the rich market of coexisting and competing flower species, flower colors, shapes, and patterns are the visual cues that allow bees to recognize and discriminate profitable species. Here we review studies concerned with the bee's use of visual signals for detecting and recognizing food sources. In the first part of the chapter, we examine the role of the bee's color vision in these tasks. In the second part, we look at the role of several spatial parameters contained in achromatic (black-and-white) stimuli.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".