Contextualising pollination benefits: effect of insecticide and fungicide use on fruit set and weight from bee pollination in lowbush blueberry
Bibliographic record
Abstract
Abstract Current approaches to determining the value of insect pollinators to crop yield assume that plants are primarily pollen limited. This is particularly relevant in a crop such as lowbush blueberry, Vaccinium angustifolium, where no fruit will set without insect‐mediated cross‐pollination. However, such valuations usually ignore other factors that are necessary to maximise crop yields. We conducted an experiment to test whether yields of lowbush blueberry attributed to pollinator activity increased independently of pest management. The experiment was a 2 × 2 factorial design, incorporating two intensities of pollination (25% or 100% of flowers), and two levels of insect and disease management with recommended fungicide and insecticide sprays (‘full inputs’ or ‘no inputs’). We demonstrated an interaction between these two factors, such that increased fruit set at harvest was only possible if 100% pollination was combined with the ‘full input’ treatment. Furthermore, increases in fruit weight among the remaining treatments were only realised in the ‘full input’ plots. These results suggest that the value accorded to pollinator activity in blueberries is strongly dependent upon pest and disease management of the crop.
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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) | 0.002 | 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".