Neonicotinoids in Honey Bee Produced in Jalisco, Mexico: Analysis of Environmental and Human Exposure
Bibliographic record
Abstract
Neonicotinoids (NN) have been used worldwide, since 1990’s as a novel insecticide. There is scientific evidence of environmental affectation, due to its systemic and persistent action. Use of NN indicates that plants translocate it into their pollen, nectar and fruits. Because these compounds cannot be washed away, NN has been detected in our food system and human body. In Mexico there are 7 NN government’s approved for domestic use, for use in pets, forestry, corps and livestock.Jalisco is an important Mexican states in food production, to include bee honey production, which has seen a 15% decline in the last years. It is suspected that NN have played a role in this decline. A lack of scientific studies regarding the use of NN’s on the environment and the particular effects on humans creates a critical need in Mexico for further assessment. In the pilot, and follow-up study, honey is used as an indicator of surrounding environmental quality determining residual levels of NN.Method: During the pilot and follow-up study, based on land uses, honey samples were collected from 30 different Jalisco locations during 1 harvesting season. Samples were analyzed via an LC-MS/MS multi-residue screen for NN concentration at ng/gr in the Agriculture and Food Laboratory from Guelph University. Results: Pilot study found pesticide (NN and organophosphates) residues in honey and wax. This indicates that the beehives, larvae and bees have been exposed to pesticides. 30 additional samples were analyzed in order to identify NN at ng/gr. Honey’s residue level of NN results were compared with EU LMR Codex Alimentarious in order to describe environmental and human exposure based on in vivo/vitro recent studies.Conclusion: This study improves our understanding about the NN environmental and human exposure through honey. We identified that the different levels of NN concentrations are related with land uses and the food produced in each region (e.g., avocado, corn, berries,).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".