The impact of lowbush blueberry (Vaccinium angustifolium Ait.) and cranberry (Vaccinium macrocarpon Ait.) pollination on honey bee (Apis mellifera L.) colony health status
Bibliographic record
Abstract
Commercial lowbush blueberry (Vaccinium angustifolium Ait.) and cranberry (Vaccinium macrocarpon Ait.) crops benefit from the presence of honey bee (Apis mellifera L.) for pollination. Unfortunately, beekeepers are observing negative impacts of pollination services on honey bee colonies. In this study, we investigated three beekeeping management strategies (MS) and measured their impact on honey bee colony health and development. Experimental groups (five colonies/MS) were: A) Control farmland honey producing MS (control MS); B) Blueberry pollination MS (blueberry MS); C) Cranberry pollination MS (cranberry MS) and D) Double pollination MS, blueberry followed by cranberry (double MS). Our goals were to 1) compare floral abundance and attractiveness of foraging areas to honey bees between apiaries using a Geographic Information System, and 2) compare honey bee colony health status and population development between MS during a complete beekeeping season. Our results show significantly lower floral abundance and honey bee attractiveness of foraging areas during cranberry pollination compared to the other environments. The blueberry pollination site seemed to significantly reduce brood population in the colonies who provided those services (blueberry MS and double MS). The cranberry pollination site seemed to significantly reduce colony weight gain (cranberry MS and double MS) and induce a significantly higher winter mortality rate (cranberry MS). We also measured significantly higher levels of Black queen cell virus and Sacbrood virus in the MS providing cranberry pollination (cranberry MS and double MS).
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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 | 0.000 | 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".