Determination of emamectin benzoate residues in topsoil and surface water of crop production areas in Thailand
Bibliographic record
Abstract
Emamectin benzoate (EB1), one of the most heavily imported agrochemicals in Thailand, ranks among the top three pesticides by import volume and expense. This study quantified EB1 residues in soil and surface water from 70 farms that applied EB1 during the 2022 rice and corn growing seasons. Samples were collected after the harvest season and analysed using high-performance liquid chromatography with ultraviolet detection at 254 nm. EB1 was detected in 97% of soil samples, with a mean concentration of 1.41 mg/kg (range: 0.05–8.82 mg/kg, standard deviation: 0.59 mg/kg). Water samples had a lower limit of qualification of 1 mg/kg, and limit of detection of 0.33 mg/kg. However, the study was limited to agricultural areas within the Nikompatthana subdistrict, Bang-Rakam district, Phitsanulok, Thailand. To broaden the research scope and possibly the generalizability of the data, future studies should expand sampling to other farms in different regions of Thailand to assess the spatial variability of EB1 contamination. Furthermore, a longitudinal study monitoring EB1 residues in soil and water over multiple growing seasons is necessary to understand the persistence and degradation of this pesticide in the environment. A comprehensive assessment of human health and the ecological risks associated with EB1 exposure should also be conducted. Additionally, strengthening regulations, raising public awareness regarding the potential risks of pesticide use, and promoting sustainable agricultural practices are crucial steps in mitigating the adverse effects of EB1.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".