Winter mortality, diversification, and self-sufficiency affect honey bee (Hymenoptera: Apidae) colony profit in Canada: a model of commercial Alberta beekeepers
Bibliographic record
Abstract
Canadian beekeepers faced widespread levels of high honey bee colony mortality over the winter of 2021/2022, with an average winter loss of 45%. To understand the economic impact of winter colony mortality in Canada and the beekeeping management strategies used to mitigate these losses, we develop a profit model of commercial beekeeping operations in Alberta, Canada. Our model shows that for operations engaging in commercial pollination as well as honey production (compared to honey production alone), per colony profit is higher and operations are better able to withstand fluctuations in exogenous variables such as prices and environmental factors affecting productivity including winter mortality rates. The results also suggest that beekeeping operations that replace winter colony losses with splits instead of package bees accrue higher per colony profit than those importing packages to replace losses. Further, operations that produce their own queens to use in their replacement splits, accrue even higher profit. Our results demonstrate that the profitability of beekeeping operations is dependent on several factors including winter mortality rates, colony replacement strategies, and the diversification of revenue sources. Beekeepers who are not as susceptible to price and risk fluctuations in international markets and imported bee risks accrue more consistently positive profits.
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 | 0.001 | 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".