Pesticide Residue and Bio-pesticides in Vegetable Crops
Bibliographic record
Abstract
Intensive vegetable production using pesticides has biggest threat to growers and the consumers. In such instances the accumulation of pesticide residues is increased due to relatively short pre-harvest interval. Use of pesticides without knowing the label claim information increases the cost of production, increases the number of spray and labour cost, ultimately leading to decrease in farmers profitability. Hence, the adoption of pesticide as per label claim is very much essential. The level of residues should be below the maximum residue limit (MRL) at the time of harvest. Most of the detected pesticides in vegetables are not registered by Central Insecticide Board and Registration committee (CIBRC) for use on that specific vegetable which is the off label use of pesticides. Crops grouping is the development of a model that allows extrapolation of residue data from a few representative crops to many other crops in the same group. This allows establishment of residue tolerances for the entire group of crops based on the residue values from certain key crops that are similar. The acceptance of representative crop is a critical component of the savings from using the crop groups. IR-4’s involvement with efforts to remove pesticide residues as a barrier for exports for US-grown specialty crops has been growing in importance over the last 20 years. By establishing a common MRL on a specialty crop from a particular crop protection product use, trade irritants between the two countries can be prevented before they have the potential to become a major problem for specialty crop growers on each side of the border. The U.S./Canadian specialty crop partnership has yielded valuable results for all the stakeholders involved. IR4 signed MOUs with Canada, New Zealand, Brazil, Costa Rica, and Colombia. This model is also much needed for India to regulate the pesticide label claims for numerous crops.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".