Setting maximum levels for lead in game meat in EC regulations: An adjunct to replacement of lead ammunition
Bibliographic record
Abstract
Each year, hunters from 12 of the 27 European Union (EU) countries and the UK shoot over 6 million large game mammals, 12 million rabbits and hares and over 80 million birds. They support an international game meat market worth over 1.1 thousand million Euros. Animals shot with lead ammunition frequently contain lead fragments in the carcass which contaminate meals made from game meat with concentrations of lead substantially above the maximum allowable level (ML) set by European Commission Regulation EC1881/2006 for meat from domesticated animals. This poses a health risk to frequent consumers of wild-shot game meat, with children and pregnant women being particularly vulnerable. Total replacement of lead rifle and shotgun ammunition with available non-toxic alternatives is needed for all hunting in EU nations to prevent exposure of humans and wildlife to ammunition-derived lead and to allow the depletion of the long-term environmental legacy of lead from spent ammunition. We propose that EC1881/2006 is amended to incorporate an ML for game meats as a supplementary measure to the replacement of lead ammunition. This would harmonise food safety standards for lead in meats traded across and imported into the EU.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".