Computer Simulation of Fate and Transport of Metolachlor in a Soil Column Study
Bibliographic record
Abstract
PRZM2 is a computer model developed to simulate pesticide transport through the soil profile under saturated and unsaturated conditions. The ability of PRZM2 to simulate the fate and transport of metolachlor will be examined and the simulation results from a soil column study will be evaluated. The input parameters and the measured data were obtained from a soil column study. The model outputs were compared to the measured values and statistical analyses were performed using the coefficient of performance approach. Based on the simulated results, metolachlor did not leach below 0.2 m depth, however, herbicide residue was detected in the soil solution at deeper depths in the soil columns. Although the simulated metolachlor results at 0.1 m depth followed the observed time and depth pattern, the model did not perform well in simulating metolachlor concentration at other depths. The statistical analyses show that the simulated metolachlor concentration was in poor agreement with the corresponding measured data over the entire experimental period. Higher or lower estimation of metolachlor concentrations by the model may be due to a simplistic treatment of matrix flow and the absence of a realistic macropore flow component. The existing mathematical formulation to handle transport and transformation processes may not adequately reflect the realistic fate and transport of metolachlor in soil. This may imply the need for further research and validation of PRZM2 for herbicides.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".