Unveiling the benefits and gaps of wild pollinators on nutrition and income
Bibliographic record
Abstract
Abstract Pollinators play a crucial role in global crop production, enhancing crop yields, nutritional value and fruit quality. However, their wild populations worldwide have been experiencing alarming declines. We investigated the contribution of wild pollinators to nutrition and farmer income in Canada, while examining the spatial distribution of pollination services. We used publicly available data on crop types, yields, nutrient content, and farm gate values, alongside information on natural habitats. Our findings suggest that wild pollinators in Canada help sustain the equivalent of approximately 24.4 million people each year in terms of nutrition and generate an annual income of nearly CAD$2.8 billion for farmers. To provide context, these estimates exceed half of the Canadian population and correspond to 5% of total national crop-related farm income. However, significant benefit gaps exist due to the lack of nearby pollinator habitat and insufficient pollination of dependent crops at a national scale. Addressing these gaps could potentially provide an additional nutrition supply for nearly 30 million equivalent people and increase farmer income by CAD$3 billion. We discuss how and where efforts focused on preserving and enhancing wild pollinator habitats, promoting sustainable farming practices, and raising awareness among stakeholders are crucial for the long-term viability of wild pollinator populations and the sustainability of agricultural systems in Canada. Our research underscores the urgent need for a national strategy aimed at safeguarding wild pollinators. Implementing such a strategy would not only contribute to strengthening local economies but also ensure the production of nutritionally essential food.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".