Veterinary Drug Residues in Seafood Inspected by the European Union, United States, Canada, and Japan from 2000 to 2009
Bibliographic record
Abstract
Veterinary drugs are used to treat or prevent a wide array of production-related diseases in aquaculture. Residues of these drugs in seafood products may pose risks to consumers, prompting governments to set drug residue tolerance levels and inspect seafood for violations of these standards. This study characterizes veterinary drug inspection policies and violations among four inspecting bodies (European Union (E.U.), United States (U.S.), Canada, and Japan), using government-collected veterinary drug violation data from 2000 to 2009. Most veterinary drug violations were detected in species that are commonly farm-raised. Asian seafood products, including shrimp and prawns, catfish (or fish sold as catfish), crab, tilapia, eel, and Chilean salmon were most frequently in violation of veterinary drug residue standards. Vietnam had the greatest number of violations among exporting countries. Concentrations of most veterinary drugs in seafood found in violation did not differ between inspecting bodies that reported drug concentrations. Transparency in seafood inspection reporting varied widely among inspecting bodies. Estimation of violations in the untested fraction of seafood was precluded by a lack of information from inspecting bodies regarding the distinction between targeted and random sampling. Increased transparency could facilitate a more rigorous characterization of public health risks from consuming imported seafood.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".