Pollen Foraging Differences Among Three Managed Pollinators in the Highbush Blueberry (Vaccinium corymbosum) Agroecosystem
Bibliographic record
Abstract
Highbush blueberry, Vaccinium corymbosum (Gray), production in British Columbia is dependent upon insect pollination for fruit yield with particular cultivars demonstrating low yields due to poor pollination. New managed species of pollinators are being developed to provide farmers with managed pollinator options beyond Apis mellifera (Linnaeus). Pollinators in highbush blueberry agricultural systems encounter a variety of nontarget floral resources that may affect the pollination received by the crop. Our study analyzed the differences in pollen foraging of honey bees and two species of managed bumblebees across nine farm sites. Corbicular pollen loads from pollen foraging workers were removed and identified to the lowest taxonomic level possible. Of the three managed pollinators, the corbicular pollen loads of Bombus huntii (Greene) contained the most blueberry pollen (52.1%), three times as much as the two other managed bee species. Fifteen morphotypes of pollen were identified from all foraging workers with Rosaceae being the most frequently gathered overall pollen type (n = 74). The noncrop pollen identified in our samples derived from plant species not common as weedy species in the agroecosystem suggesting that floral resource diversity outside of the farm boundaries is important to pollinators. The three managed species in our blueberry fields utilized floral resources differentially underscoring the importance of pollinator species' characteristics and large-scale floral resource landscape in developing new managed pollinators and pollination strategies.
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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 | 0.001 | 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".