Poor air quality raises mortality in honey bees, a concern for all pollinators
Bibliographic record
Abstract
Human well-being relies on the presence and role of pollinators, as they contribute to the vitality of ecosystems, support the reproduction of wild plants, increase crop yields, and strengthen overall food security. While wild bee populations are dwindling due to climate and environmental change, there has been a notable 45% rise globally in the number of managed honey bee (Apis mellifera) colonies over the past five decades. Given their economic significance and their relative ease of tracking, honey bees have the potential to serve as bioindicators of global pollinator health. Consequently, honey bees have emerged as a keystone species requiring protection and conservation efforts. Here, we investigate the intricate relationship between air quality, environmental factors, and honey bee mortality across Canada and the United States. Using statistical and machine learning modeling, our findings underscore the honey bee’s role as a bioindicator. We found that air quality is an important predictor of honey bee mortality. The risk of honey bee mortality increased with poor air quality (ozone and Air Quality Health Index) but was substantially reduced in regions with greater vegetation availability (Normalized Difference Vegetation Index). Therefore, our study offers a beacon of hope: improving management practices by increasing greenery can significantly mitigate the impact of deteriorating air quality on honey bees, providing a vital solution to safeguard our essential pollinators. Increased ozone concentration and air quality health index increases the probability of honey bee mortality across Canada and the USA, according to a machine learning model study utilising apiary tracking data and environmental data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".