The impact of planting buckwheat strips along lowbush blueberry fields on beneficial insects
Bibliographic record
Abstract
Pollination and pest control are important in many agroecosystems. Beneficial insects that provide these services (e.g., bees and natural enemies) often require floral resources beyond crop bloom. Planting floral resources along crop field margins may be a useful tactic to support communities of beneficial insects in agroecosystems. We examined the effect of planting buckwheat (Fagopyrum esculentum Moench) along lowbush blueberry (Vaccinium angustifolium Aiton) field margins on beneficial insect abundance and generic richness. We found that bee abundance was higher in buckwheat transects than control transects in 2014 and 2015, but not 2016, and that bee generic richness was higher in buckwheat transects than in control transects in 2015 only. High variability occurred across years. All bee genera recorded during blueberry bloom were also collected in buckwheat transects, suggesting buckwheat is a useful resource for the bee community involved in blueberry pollination. The effect of buckwheat on natural enemies was variable and inconsistent. We conclude that buckwheat influenced bee and natural enemy communities during certain years, but field edges in the lowbush blueberry fields studied may already adequately support beneficial insects. Thus, not all habitat management efforts with augmentative floral plantings may consistently boost communities of beneficial insects.
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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 | 0.001 |
| 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".