Oxidative Stress in two pollinators from Different Landscapes based on different pesticide residue profiles
Bibliographic record
Abstract
Oxidative stress (OX) is a state of imbalance between antioxidants and reactive oxygen species, which are the byproducts of oxidative phosphorylation in the mitochondria. Different landscapes: conventional, organic, and roadside use different pesticides and pest control management practices pest control. The roadside landscape is a control for the minimum application of herbicides and management. Two important pollinators, honey bees (Apis mellifera) and small carpenter bees (Ceratina calcarata) are studied here for their abiotic stress from different landscapes. It is unknown whether the differences in these landscapes affect the oxidative stress of these two pollinators. Conventional farms offer the most exposure to pesticides, which have been related to increased stress levels. We hypothesized that honey bees and small carpenter bees (SCB) from conventional farms would experience the highest levels of OX. Results from the lipid assay indicated that honey bees and SCB from organic and roadside landscapes experienced the lowest and highest OX levels. The results of the protein carbonyl assay revealed a significant difference between conventional and organic bees (p < 0.05), based on Tukey post-hoc test. Previous research has shown that feral bees have a tolerance to high stress levels. The same could be true of honey bees and SCB from roadside landscapes, where the conditions imitate natural habitats. Organic farms use naturally derived pesticides and moderate management practices, likely contributing to their lower levels. Examining how these pollinators are affected by different farm landscapes can reveal the benefits and detriments of their corresponding management practices. This will lead to streamlined practices that are healthier for pollinators.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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 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".