Tracking movement dynamic of fenitrothion and thiobencarb in rice paddy using a field lysimeters at different levels of soil depth
Bibliographic record
Abstract
In this study, the movement dynamic of fenitrothion (50% EC) and thiobencarb (50% EC) was investigated using the field lysimeter in the presence of rice plant at four different levels of soil depth. Iodide was used as an indicator of the mobility of these pesticides through the soil in the field lysimeter. Iodide was detected in the leachates collected at level 1 and 2 only, the concentration of iodide collected from level 2 was more than those collected from level 1. The highest breakthrough curve for fenitrothion or thiobencarb was produced from the level 4 (deep level) followed by level 3 while the breakthrough curve of level 1 was the lowest peak. Significant differences were observed among the cumulative amounts of fenitrothion or thiobencarb collected from different depth levels. The pesticide residues in the leachates increase with the depth of soil profile increase. The cumulative amounts of the two tested pesticides were compatible with the concentration of treatments, and were higher in high-treatment (50 μg/g soil) compared with that in low-treatment (25 μg/g soil). Our results obtained leaching of thiobencarb was slightly higher than the leaching of fenitrothion. These results are useful in understanding the movement of pesticides and agrochemicals in the agricultural environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".