Pollination practices and grower perceptions of managed bumble bees (<i>Bombus spp.</i>) as pollinators of cranberry in Quebec and Wisconsin
Bibliographic record
Abstract
Abstract Globally, honey bees are the most utilized animal pollinator in agriculture. However, fluctuations in honey bee colony availability have led to a demand for diversification among crop pollinators. Managed bumble bees are commercially available and highly efficient at pollinating many crops, including cranberries, yet utilization of these managed bees has remained relatively low in North America, with the cranberry industry remaining heavily reliant on honey bees. Here, we surveyed growers from Wisconsin (WI) and Quebec (QC), two of the world's largest cranberry producers, to assess their current crop pollination practices and attitudes regarding managed bumble bees as crop pollinators. To this end, we inquired about their farm demographics, usage of pollination practices, factors influencing those pollination practices, sources of information on crop pollination, and perceptions of managed bumble bees. QC respondents placed a greater importance on their relationships with beekeepers than WI respondents, while WI respondents were more concerned about fruit quality than QC respondents. QC respondents also stocked bumble bees and planted pollinator gardens at a higher percentage than WI respondents, believed that honey bees are more efficient pollinators of cranberry than bumble bees, and a greater proportion of QC respondents reported feeling well informed about bumble bees compared to WI respondents. Importantly, respondents in both regions rank bumble bees' ability to pollinate in inclement weather as their greatest benefit, and the costs of bumble bees as the greatest barrier to their use. We propose that trusted sources of pollination information in both regions, including university specialists, crop consultants, and beekeepers, are well suited to clarify misconceptions regarding bumble bee pollination.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".