Limitations of European Union policy and law for regulating use of lead shot and sinkers: comparisons with North American regulation
Bibliographic record
Abstract
Abstract Extensive shooting and angling causes, indirectly, fatal lead poisoning of birds. European Union policy on this major source of pollution is inconsistent with its laws regulating other forms of lead in the environment. Only three countries have banned the use of lead shot completely in the European Union, despite availability of substitutes and evidence that their use is a major contributor to bird conservation. The European Commission uses the criterion of amount of lead deposited and corroded, and the concentration of lead in water and soil, as the basis of their decisions. The USA and Canada used the prevalence of lead poisoning among birds as the basis of policy and law allowing them to reduce lead use at the continental level. The EU and North American policies and law on lead reduction are compared in this study, on the basis of which recommendations are developed indicating how the EU could revise its approach and resolve this environmental problem. Reluctance to act on lead reduction by the European Parliament and its member states reflects the current vested interests of the sporting communities. Companies in eight European countries already produce non‐toxic materials for hunting and shooting, and are not the limiting factor in this issue. Copyright © 2009 John Wiley & Sons, Ltd and ERP Environment.
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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 | 0.096 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".