The transition to non-lead sporting ammunition and fishing weights: Review of progress and barriers to implementation
Bibliographic record
Abstract
This review presents evidence of lead exposure and toxicity to wildlife and humans from spent shotgun and rifle ammunition and fishing weights, and the barriers and bridges to completing the transition to non-lead products. Despite the international availability of effective non-lead substitutes, and that more jurisdictions are adopting suitable policies and regulations, a broader transition to non-lead alternatives is prevented because resolution remains divided among disparate human user constituencies. Progress has occurred only where evidence is most compelling or where a responsible public authority with statutory powers has managed to change mindsets in the wider public interest. Arguments opposing lead bans are shown to lack validity. Differing national regulations impede progress, requiring analysis to achieve better regulation. Evidence that lead bans have reduced wildlife exposure should be used more to promote sustainable hunting and fishing. Evidence of the lead contribution from hunted game to human exposure should shape policy and regulation to end lead ammunition use. The Special Issue presents evidence that a transition to non-lead products is both warranted and feasible.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".