The countryside or the city: Which environment is better for the honeybee?
Bibliographic record
Abstract
For a number of years, the decline of honeybee (Apis mellifera) in North America and Europe has been the subject of much debate. Among the many factors proposed by hundreds of studies to explain this phenomenon is the hypothesis that agricultural activities using pesticides contribute to the weakness of bee colonies. Moreover, while urban beekeeping is presently booming in several cities, we do not know if this environment is more beneficial for bees than the typical, rural area. In the summer of 2018, we sampled honeybees (foragers and larvae) in rural (Laurentians) and urban (city of Montreal) areas and compared them using the following biomarkers: carotenoids, retinoids, α-tocopherol, metallothionein-like proteins (MTLPs), lipid peroxidation, triglycerides, acetylcholinesterase activity (AChE) and proteins. Pesticides, pharmaceuticals and personal care products (PPCPs) and metals were also quantified in honeybees' tissues. Our result revealed that, globally, urban foragers had higher levels of insecticides and PPCPs and that metals were in greater concentrations in urban larvae. Compared to rural foragers, urban foragers had higher concentrations of MTLPs, triglycerides, protein and AChE activity. The multifactorial analysis confirmed that insecticides, some metals and PPCPs were the most influential components in the contaminant‒biomarker relationships for both foragers and larvae.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".