Reduced abundance and earlier collection of bumble bee workers under intensive cultivation of a mass‐flowering prairie crop
Bibliographic record
Abstract
Abstract One of the most commonly seeded crops in Canada is canola, a cultivar of oilseed rape (Brassica napus). As a mass‐flowering crop grown intensively throughout the Canadian Prairies, canola has the potential to influence pollinator success across tens of thousands of square kilometers of cropland. Bumble bees (Bombus sp.) are efficient pollinators of many types of native and crop plants. We measured the influence of this mass‐flowering crop on the abundance and phenology of bumble bees, and on another species of social bee (a sweat bee; Halictus rubicundus), by continuously deploying traps at different levels of canola cultivation intensity, spanning the start and end of canola bloom. Queen bumble bees were more abundant in areas with more canola cover, indicating that this crop is attractive to queens. However, bumble bee workers were significantly fewer in these locations later in the season, suggesting reduced colony success. The median collection dates of workers of three bumble bee species were earlier near canola fields, suggesting a dynamic response of colonies to the increased floral resources. Different species experienced this shift to different extents. The sweat bee was not affected by canola cultivation intensity. Our findings suggest that mass‐flowering crops such as canola are attractive to bumble bee queens and therefore may lead to higher rates of colony establishment, but also that colonies established near this crop may be less successful. We propose that the effect on bumble bees can be mitigated by spacing the crop more evenly with respect to alternate floral resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".