Pesticide Metabolism in Plants and Microorganisms: An Overview
Bibliographic record
Abstract
Understanding pesticide metabolism in plants and microorganisms is a key component for the development, the safe and efficient utilization of these compounds, and for bioremediation of these chemicals in contaminated soil and water. Selective metabolism of pesticides in non-target species (e.g., crop plants) and sensitivity in target species (e.g. weeds, insects and pathogen pests), is the basis of chemical pest control. Pesticide biotransformations may occur via metabolism or co-metabolism. Metabolism of a given pesticide in plants and microorganisms is generally a multi-step process. Individual components of such degradation/detoxification pathways include: oxidation, reduction, hydrolysis and conjugation. Pathway diversity depends on the chemical structure of the xenobiotic compound, the organism, environmental conditions, and metabolic factors regulating expression of these biochemical pathways. Knowledge of these enzymatic processes, especially concepts related to mechanism of action, resistance, selectivity, tolerance, and environmental fate has advanced pesticide science. One example is the development of herbicide tolerant crops. Advances in pesticide metabolism have also been facilitated by use of improved analytical techniques, molecular biological approaches, and immunological tools.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".