Detection and Concentration of Neonicotinoids and Other Pesticides in Honey from Honey Bee Colonies Located in Regions That Differ in Agricultural Practices: Implications for Human and Bee Health
Bibliographic record
Abstract
This is a preliminary study conducted to analyze the presence and concentration of pesticides in honey obtained from honey bee colonies located in two regions with managed ecosystems that differ in the intensity and technification of agricultural practices. Fourteen pesticides at variable concentrations were detected in 63% of the samples analyzed. The pesticides most frequently found at higher concentrations were insecticides (neonicotinoids, followed by organophosphates), herbicides, and fungicides. The number, frequency, and concentration of pesticides were higher in samples collected from hives located where intensive and highly-technified agriculture is practiced. Forty-three percent of the samples from that zone had residues of imidacloprid, compared with only 13% of the samples from the less-technified zone. Furthermore, 87.5% of those samples had imidacloprid concentrations that were above sublethal doses for honey bees (>0.25 ng/g) but that are not considered hazardous to human health by the European Commission. The results of this study suggest that honey can be used as a bioindicator of environmental contamination by pesticides, which highlights the need to continue monitoring contaminants in this product to determine the risks of pesticide impacts on pollinator health, on ecosystems, and on their potential implications to human health and other non-target organisms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".