Gene-Centric Model Approaches for Accurate Prediction of Pesticide Biodegradation in Soils
Bibliographic record
Abstract
Pesticides are widely used in agriculture despite their negative impact on ecosystems and human health. Biogeochemical modeling facilitates the mechanistic understanding of microbial controls on pesticide turnover in soils. We propose to inform models of coupled microbial dynamics and pesticide turnover with measurements of the abundance and expression of functional genes. To assess the advantages of informing models with genetic data, we developed a novel "gene-centric" model and compared model variants of differing structural complexity against a standard biomass-based model. The models were calibrated and validated using data from two batch experiments in which the degradation of the pesticides dichlorophenoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyacetic acid (MCPA) were observed in soil. When calibrating against data on pesticide mineralization, the gene-centric and biomass-based models performed equally well. However, accounting for pesticide-triggered gene regulation allows improved performance in capturing microbial dynamics and in predicting pesticide mineralization. This novel modeling approach also reveals a hysteretic relationship between pesticide degradation rates and gene expression, implying that the biodegradation performance in soils cannot be directly assessed by measuring the expression of functional genes. Our gene-centric model provides an effective approach for exploiting molecular biology data to simulate pesticide degradation in soils.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".