How nutrition and energy needs affect bumble bee pollination services: A mathematical model
Bibliographic record
Abstract
Abstract The balance between nutrition and energy needs has an important impact on the spatial distribution of foraging animals. In the present paper, we focus on the case of bumble bees moving around a landscape in search of pollen (to meet nutritional needs) and nectar (to meet energy needs). Depending on the colony demands, bumble bees can concentrate their foraging effort towards either pollen rich flower species or nectar rich flower species. This behaviour allows us to establish a strategy – a spatial landscape design – which can maximize the pollination services of crops that are nutritionally deficient for pollinators by adding nutritionally rich wildflower patches. To do this, we formulate a mathematical partial integro-differential equation model to predict the spatial distribution of foraging bumble bees. We use our model to predict the location, composition and quantity of the wildflower patches adjacent to crop fields that will be most beneficial for crop pollination services. Our results show that relatively small quantities of wildflowers in specific locations with respect to the nest sites and the crop can have a positive impact on pollination services when the composition (i.e., pollen to nectar ratio) of the added wildflowers is significantly different from the composition of the existing crop flowers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".