Habitat assessment ability of bumble-bees implies frequency-dependent selection on floral rewards and display size
Bibliographic record
Abstract
Foraging pollinators could visit hundreds of flowers in succession on mass-flowering plants, yet they often visit only a small number--potentially saving the plant from much self-pollination among its own flowers (geitonogamy). This study tests the hypothesis that bumble-bee (Bombus impatiens) residence on a particular plant depends on an assessment of that plant's reward value relative to the overall quality experienced in the habitat. In a controlled environment, naive bees were given experience in a particular habitat (all plants having equal nectar quality or number of rewarding flowers), and we tested whether they learn about and adaptively exploit a new habitat type. Bees' residence on a plant (number of flowers probed per visit) was eventually invariant to a doubling of absolute nectar quality and increased only slightly with a doubling of absolute flower number in the habitat. These results help to explain why pollinators are quick to leave highly rewarding plants and suggest that the fitness of rewarding plant traits will often be frequency dependent. One implication is that geitonogamy may be a less significant constraint on the evolution of rewarding traits than generally supposed.
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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 | 0.002 |
| 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 | 0.001 | 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".