Identifying and modeling the impact of neonicotinoid exposure on honey bee colony profit
Bibliographic record
Abstract
Pollination by the European honey bee, Apis mellifera, is essential for the production of many crops, including highbush blueberries (Vaccinum corymbosum). To understand the impact of agrochemicals (specifically, neonicotinoids, a class of synthetic, neurotoxic insecticides) on these pollinators, we conducted a field study during the blueberry blooms of 2020 and 2021 in British Columbia (B.C.). Forty experimental honey bee colonies were placed in the Fraser Valley: half of the colonies were located within 1.5 km of highbush blueberry fields ("near" colonies) and half were located more than 1.5 km away ("far" colonies). We calculated risk quotients for these compounds using their chronic lethal dietary dose (LDD50) and median lethal concentration (LC50). Pesticide risk was similar between colonies located near and far from blueberry forage, suggesting that toxicity risks are regionally ubiquitous. Two systemic neonicotinoid insecticides, clothianidin and thiamethoxam, were found at quantities that exceeded chronic international levels of concern. We developed a profit model for a pollinating beekeeper in B.C. that was parameterized by: detected pesticide levels; lethal and sublethal bee health; and economic data. For colonies exposed to neonicotinoid pesticides in and out of the blueberry forage radii, there were economic consequences from colony mortality and sublethal effects such as a loss of honey production and compromised colony health. Further, replacing dead colonies with local bees was more profitable than replacing them with imported packages, illustrating that beekeeping management selection of local options can have a positive effect on overall profit.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".