Honey bee‐collected pollen in agro‐ecosystems reveals diet diversity, diet quality, and pesticide exposure
Bibliographic record
Abstract
Abstract European honey bees Apis mellifera are important commercial pollinators that have suffered greater than normal overwintering losses since 2007 in North America and Europe. Contributing factors likely include a combination of parasites, pesticides, and poor nutrition. We examined diet diversity, diet nutritional quality, and pesticides in honey bee‐collected pollen from commercial colonies in the Canadian Maritime Provinces in spring and summer 2011. We sampled pollen collected by honey bees at colonies in four site types: apple orchards, blueberry fields, cranberry bogs, and fallow fields. Proportion of honey bee‐collected pollen from crop versus noncrop flowers was high in apple, very low in blueberry, and low in cranberry sites. Pollen nutritional value tended to be relatively good from apple and cranberry sites and poor from blueberry and fallow sites. Floral surveys ranked, from highest to lowest in diversity, fallow, cranberry, apple, and blueberry sites. Pesticide diversity in honey bee‐collected pollen was high from apple and blueberry sites and low from cranberry and fallow sites. Four different neonicotinoid pesticides were detected, but neither these nor any other pesticides were at or above LD 50 levels. Pollen hazard quotients were highest in apple and blueberry sites and lowest in fallow sites. Pollen hazard quotients were also negatively correlated with the number of flower taxa detected in surveys. Results reveal differences among site types in diet diversity, diet quality, and pesticide exposure that are informative for improving honey bee and land agro‐ecosystem management.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".