Review of National Residue Control Programme for Aquaculture Drugs in Selected Countries
Bibliographic record
Abstract
Residues of drugs in aquaculture-raised products could potentially cause health hazards for consumers.Most seafood importing countries have regulations on maximum residue limits (MRL) for veterinary drugs in aquaculture products.National MRLs are generally based on Codex and where there are no Codex recommendations, countries may develop MRLs based on risk assessments.Most importing countries have regulations that require aquacultureproducing countries to demonstrate compliance by implementing a National Residue Monitoring Programme (NRMP).To understand the regulations and implementation of NRMP in seafood exporting and importing countries, an analysis was made on the regulations in Canada and EU and NRMP implementation in four major exporting countries; China, Viet Nam, Malaysia and Philippines.Data source were from websites of seafood inspection agencies in the countries and reports of inspection from EU Food and Veterinary Office (FVO).All seafood exporting countries have harmonised their regulations with that of EU and data on the implementation of NRMP is available from these countries.The regulatory pressure from the importing countries seems to drive NRMP implementation in the exporting countries.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".