Biochemical Conjugation of Pesticides in Plants and Microorganisms: An Overview of Similarities and Divergences
Bibliographic record
Abstract
Metabolic systems protect organisms from toxic substances. Conjugation reactions, found in fungi, bacteria and plants, not only detoxify metabolic wastes, but also form structural molecules and act to regulate hormone action. Conjugation has been defined by Dorough (1976) as a metabolic process whereby endogenous and exogenous chemicals are converted to polar components facilitating their removal from site(s) of continuing metabolism. However, pesticide conjugates are not always made more polar e.g., 2,4-D-leucine in soybean ( Glycine max ) and methylated arsenic and mercuric compounds in microbes are less polar. Generally, enzymes involved in conjugation are not substratespecific, i.e., they detoxify both exogenous and endogenous compounds. There are many differences among the types of conjugates found in plants and soil microbes (fungi, bacteria). In plants, sugar and amino acid conjugates are formed, whereas, in nutrient-limited soil microbes, sugars and amino acids are rarely available for conjugation. Microbes use different endogenous substrates produced from continuing metabolism, e.g. methyl and acyl conjugates from methanogenesis. Conjugation reactions also confer herbicide selectivity in plants. These conjugates may be moved into the vacuole and/or incorporated in the cell wall matrix/vascular tissue. Fate of pesticides in soil may also be affected by conjugation. Conjugates may be immobilized becoming biologically unavailable, or made more recalcitrant and lipophilic and accumulate in the food chain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".