Comparison of Removal Efficiencies of Different Household Cleaning Methods in Reducing Imidacloprid and Triabendazole Residues on Cherry Tomatoes
Bibliographic record
Abstract
People have paid much attention on the pesticide residues in agricultural products at present. However there are less concern on processed agricultural products or on final consumption of processed foods, even though most processed foods are being finally consumed. In this paper, pesticide residues such as imidacloprid and triabendazole on the cherry tomatoes were cleaned by conducting different household cleaning methods in the way of normal vegetable cleaning. Results showed that it can effectively remove the residual imidacloprid on cherry tomatoes by soaking first them in water and then rinsing them with running tap water, wherein the removal rates were 30.59%-65.24% and processing factor were 0.3476-0.6941. While to remove the residual triabendazole on cherry tomatoes, we first soaked them in 0.1% edible vinegar solution and then rinsed them with running tap water, which can also effectively remove the triabendazole residues on the cherry tomatoes, with removal rates reaching 29.31%-74.01% and processing factor reaching 0.2599-0.7069. Our research provides an inherent relationship between pesticide residues and cleaning approaches as well as important theoretical basis for risk assessments of agricultural food.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".