A review of the enhanced degradation of pesticides in tropical agricultural soils
Bibliographic record
Abstract
Pesticides newly introduced into the soil are normally poorly degraded by native soil microbes. However, studies have demonstrated that repeated exposure of pesticides to soil microbes potentially enhances their biodegradation through selective enrichment of pesticide-metabolising microorganisms, particularly when the compound is used as a carbon (C) or nitrogen (N) and energy source. Enhanced degradation of recalcitrant compounds in soil has a significant environmental impact, as the chemicals are less likely to contaminate environmental ecosystems. The authors have undertaken several studies to isolate these adapted microbes that rapidly degrade chemicals hitherto known to be recalcitrant in soil. These microbes could potentially be used for bioremediation (bioaugmentation). In addition, other studies have shown their potential to remove pesticide contamination from the environment by the use of organic materials locally generated as organic amendments (biostimulation). In this review, the various methods used in the course of the authors’ studies in determining the utilisation of selected chemicals (pesticides) by adapted microbes as a source of carbon and nitrogen for growth and energy are discussed. This review also presents some of the compounds that the authors have worked with and the successes registered in isolating key degraders of the respective pesticides and the extent to which the locally generated organic materials are able to enhance the degradation of the respective chemicals in soil.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".