Identity and diversity of pollens collected by two managed bee species while in blueberry fields for pollination
Bibliographic record
Abstract
The nutritional needs and foraging behavior of managed bees often lead to pollen collection from flowers other than the focal crop during crop pollination. To understand the pollen needs and preferences of managed bees during blueberry pollination, we identified pollen collected by Apis mellifera Linnaeus, 1758 (Hymenoptera: Apidae) and Bombus impatiens Cresson, 1863 (Hymenoptera: Apidae) colonies across two years. Bumble bees collected a wider diversity of pollens compared to honey bees, whereas honey bees were more focused on abundant resources. Despite blueberries being the most abundant resource in the landscape, it was not the most collected pollen by either bee species in 2018. However, it was the most collected pollen by bumble bees in 2019 and they collected substantially more blueberry pollen than honey bees in both years. In 2018, buckthorn, Rhamnus L. (Rosales: Rhamnaceae) or Frangula Mill. (Rosales: Rhamnaceae), and willow, Salix L. (Malpighiales: Salicaceae), pollens were abundantly collected by both bee species. In 2019, cherry, Prunus L. (Rosales: Rosaceae), and willow (Salix) pollens were collected at high proportions by both species. Brambles, Rubus L. (Rosales: Rosaceae), and white clover, Trifolium repens L. (Fabales: Fabaceae), were also common pollen sources for honey bees, whereas oak, Quercus L. (Fagales: Fagaceae), was collected by bumble bees. Landscape analyses also revealed that certain land cover types were positively correlated with the collection of preferred pollen types. Herbaceous wetlands were associated with collection of buckthorn (Rhamnus/Frangula), willow (Salix), and cherry (Prunus) pollen, which were primary pollen resources for both bee species. There was no correlation between landscape diversity and pollen diversity, suggesting that colonies forage based on nutritional requirements rather than resource availability.
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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 | 0.001 | 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) | 0.001 | 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".