Pesticide Residue and Bio-pesticides in Vegetable Crops
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it