Population origin, body mass, and viral infections influence drone honey bee ( <i>Apis mellifera</i> ) heat tolerance
Bibliographic record
Abstract
Abstract Extreme temperatures associated with climate change are expected to impact the physiology and fertility of a variety of insects, including honey bees. Most previous work has focused on female honey bees, and comparatively little research has investigated how heat exposure affects males (drones). To address this gap, we tested how body mass, viral infections, Africanization, and geographic origin (including stocks from Australia, California, and Ukraine as well as diverse locations within British Columbia, Canada) influenced drone and sperm heat tolerance. We found that individual body size was highly influential, with heavier drones being more likely to survive a heat challenge than smaller drones. Drones originating from feral colonies in Southern California (which are enriched for African genetics) were also more likely to survive a heat challenge than drones originating from commercially-supplied Californian stock. We found no association between drone mass and thermal tolerance of sperm over time in an in vitro challenge assay, but experimental viral infection decreased the heat tolerance of sperm. Overall, there is ample variation in sperm heat tolerance, with sperm from some groups displaying remarkable heat resilience and sperm from others being highly sensitive, with additional factors influencing heat tolerance of the drones 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 | 0.000 | 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) | 0.002 | 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".