Nitenpyram degradation in finished drinking water
Bibliographic record
Abstract
RATIONALE: Neonicotinoid pesticides and their metabolites have been indicated as contributing factors in the decline of honey bee colonies. A thorough understanding of neonicotinoid toxicity requires knowledge of their metabolites and environmental breakdown products. This work investigated the rapid degradation of the neonicotinoid nitenpyram in finished drinking water. METHODS: Nitenpyram reaction products were characterized using liquid chromatography/quadrupole time-of-flight mass spectrometry (LC/QTOFMS). A software algorithm that compared degraded and control samples was utilized to facilitate efficient data reduction. Fragmentation pathways for six reaction products and nitenpyram were proposed using predictive software and manual product ion analysis. RESULTS: This study showed that nitenpyram degradation in unpreserved finished drinking water was likely the result of oxidation, hydrolysis and reaction with Cl2 . Structures for six nitenpyram reaction products were proposed that suggest the C9/C11 olefin as the key reactive site. CONCLUSIONS: Similarities between the identified nitenpyram reaction products and imidacloprid metabolites highlight the importance of this study, as the toxicity of neonicotinoids to pollinators has been linked to their metabolites. Based on the proposed reaction mechanisms, the identified nitenpyram reaction products in finished drinking water could also be present in the environment and water treatment facilities. As such, identifying these degradation products will aid in evaluating the environmental impact of neonicotinoid pesticides. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".