Bibliographic record
Abstract
Did you know that bumble bee queens sleep throughout the winter in an underground home in the soil? Finding the best real estate is essential for the survival of bumble bee queens during the harsh season and seals the fate of future generations. This is why the choice of winter home for the queens is so important, but very little is known about how these bees choose the location of their winter homes. For example, the type of soil is a major choice for a bee that is looking for a spot to overwinter because some locations may not be ideal; they might be too hard to dig in or may be contaminated with pollution. Some pesticides that remain in soils can harm queen bees, so Sabrina Rondeau and Nigel Raine of the University of Guelph, Canada, decided to test if queen bees avoid harmful chemicals when looking for winter homes.First, the duo set up crates of soil, which they had contaminated with pesticides that are commonly encountered in agricultural soils, inside a large tent enclosure. Some of the crates had low pesticide levels, which are typically found in nature, while others had higher levels of pollution. In addition, the team left some of the containers of soil unpolluted to see if the queen bees would prefer to nest in clean soil and avoid the pesticide contamination altogether. Then the researchers covered the soil with mesh, to stop any bees from digging in it, and purchased commercial bumble bee colonies before releasing them into the enclosure, to allow the queens to mate and familiarise themselves with the cases of soil as they prepared to hibernate. After a month, the researchers removed the mesh from the crates so the queen bees could access the soil and create their nests. Then, over a 4-week period, the duo carefully searched each crate for hibernating queen bees to discover which soils the insects had selected.You'd think that queens would avoid anything that could cause them harm, but Rondeau and Raine found the complete opposite. Queen bees preferred the soils that were contaminated with pesticides. This discovery opens many questions for the researchers to address. For example, why would bees prefer contaminated soils which potentially pose a risk? Perhaps the chemicals get rid of other dangers to hibernating bees, such as fungus or other microbes. Or the chemicals might be perceived as something new and exciting that the queens would want to explore.This study reveals an interesting, but concerning, discovery. While surviving winter for queen bees is already a challenge, harsh conditions paired with chemical contamination of the soil in which they make their homes is concerning for the future of bumble bee colonies. Now we know that soil contamination poses such a risk to bumble bees, perhaps we can investigate new ways to save bees and begin by using pesticides more sparingly to lessen the detrimental impacts on hibernating queen bees.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".