Effect of a colour dimorphism on the flower constancy of honey bees and bumble bees
Bibliographic record
Abstract
We assessed the flower constancy of Italian honey bees (Apis mellifera ligustica Spinelli, 1808) and bumble bees (Bombus impatiens Cresson, 1863) by presenting individual foragers with a mixed array of equally rewarding yellow and blue flowers after they were trained to visit each colour in succession. All honey bees showed a high degree of flower constancy to one colour and rarely visited the alternate colour, whereas most bumble bees indiscriminately visited both colours. Foraging rates (flowers visited per minute) and flower handling times did not differ between honey bee and bumble bee foragers; however, bumble bees tended to fly farther between consecutive flower visits and make fewer moves to nearest neighbouring flowers than honey bees. When bees were forced to specialize on one of two previously rewarding flower colours by depleting one colour of reward, honey bees required almost twice as many flower visits to specialize on the rewarding flower colour as bumble bees. Together, these results suggest that the relationship between individual flower constancy and colour differences is not a general behavioural phenomenon in honey and bumble bees, perhaps because of differences in the ability of each group to effectively manage multiple colours at the same time and location.
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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) | 0.000 | 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) | 0.002 | 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".