Present Situation and Development Trend of MRLs for Pesticides in and outside China
Bibliographic record
Abstract
Introduced present situation in development of Maximum Residue Limits(MRLs) for pesticides in food and farm produce in Codex Alimentarius Commission(CAC),EU,USA,Japan,Canada,Australia,New Zealand and China,analyzed their last development,composition and trend,pointed out the gap of pesticide MRLs in China as follows:few MRLs,lacking harmony and risk assessment as well as its basic research,unsound mechanism for standard establishing and revising,standard updating is not in time.Improvement suggestions to resolve the problems the above mentioned including:setting up long-term plan,strengthening study and investigation of basic data for establishing and revising MRLs,harmonizing department relations between standard establishment and pesticide registration and administration,intensifying international corporation and intercommunion,griping in time the last developments on MRLs and food safety management in related international organization and developed countries.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".