Pollination potential of male bumble bees (<i>Bombus impatiens</i>): movement patterns and pollen-transfer efficiency
Bibliographic record
Abstract
Many plant species rely on female bumble bee workers for pollen transfer. However, male bumble bees, which differ both behaviourally and morphologically from female workers, also visit many species of flowering plants and may transfer pollen differently. Males can outnumber workers on some plants, particularly those that flower late in the season. In laboratory experiments, we compared the movement patterns of male bees and female workers on an artificial flower array. We also compared the pollen transfer efficiency of males and workers foraging on Brassica rapa flowers. Males travelled between patches of flowers more often than workers, which may be an effective method for reducing geitonogamy in plants. Males also had lower foraging rates, longer flower handling time, and transferred more pollen from one B. rapa flower to the next than workers did. These caste-based differences in pollinating behaviour suggest that, under certain circumstances and on a per-visit basis, male bumble bees may be better pollen vectors than female foragers. Furthermore, our results emphasize the need to avoid species-wide generalizations of pollinator effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".