Agricultural field margins provide food and nesting resources to bumble bees ( <i>Bombus</i> spp., Hymenoptera: Apidae) in Southwestern Ontario, Canada
Bibliographic record
Abstract
Abstract Bumble bees are declining globally, largely due to habitat loss driven by agricultural intensification. Within agriculturally dominated landscapes, semi‐natural habitats (e.g. meadows, wetland edges) are fragmented, increasing the value of uncropped agricultural field margins for providing a source of food, nesting, and hibernation resources to bumble bees. We compared bumble bee communities sampled in agricultural field margins and semi‐natural habitats across Southwestern Ontario, Canada in order to assess differences in habitat quality. We then examined the effect of floral resources and soil characteristics on the bumble bee communities present in each site, independent of the habitat type classification. Our data revealed that bumble bee abundance, diversity, and community composition did not differ between habitat types. However, when examined independently of habitat type, bumble bee abundance and diversity increased with floral abundance, floral diversity, the number of rodent holes, and sandy soil texture. These results suggest that agricultural field margins are not inherently degraded bumble bee habitat and have the potential to provide food and nesting resources comparable to semi‐natural habitats in a fragmented agricultural landscape. Ensuring field margins contain the necessary floral resources and soil characteristics for bumble bees has implications for conservation and mitigating habitat loss caused by the agricultural fields themselves.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".