Health and Environmental Risks from Lead-based Ammunition: Science Versus Socio-Politics
Bibliographic record
Abstract
Lead (Pb) is toxic and is banned from gasoline, paints, and various household items in most developed countries.Lead ammunition, however, is still widely used for hunting and shooting, and is now likely the greatest, largely unregulated source of lead that is knowingly discharged into the environment in the USA (Health Risks from Lead-Based Ammunition in the Environment-A Consensus Statement of Scientists 2013; U.S. Geological Survey 2013).For decades, poisoning from spent lead ammunition was mainly regarded as a disease of waterfowl (Bellrose 1959), but it also puts at risk the health of raptors, scavengers, and other terrestrial species, including humans who frequently consume hunted game (Fig. 1).Scientists across North America and Europe have published consensus statements on the risks to wildlife, the environment and human health from the use of lead ammunition, and the need for its replacement by non-toxic alternatives (Health Risks from Lead-Based Ammunition in the Environment-A Consensus Statement of Scientists, 2013; Group of Scientists 2014).This is now a pressing One Health issue.We carried out a literature search in the database Web of Science for scientific papers dealing with environmental and health consequences of the use of lead in ammunition.We used 11 different query combinations of the key words ''lead, lead-free, non-lead, non-toxic, ammunition, hunting, poisoning, shot, meat, game, raptor, waterfowl, and upland game.''After removing non-relevant papers, we manually added approximately 100 references found by searching in other databases (PubMed, Google Scholar) or in reference lists of published literature.Finally, we were left with 570 peer-reviewed papers published from 1975 through August 2016.The number of articles per year showed a strong increase over time during the period covered: 6.9 in 1975-1989, 9.3 in 1990-1999, 19.0 in 2000-2009, and 27.7 in 2010-2016.These papers were analyzed for relevance and conclusions, with special reference to topics such as health risks for humans consuming game hunted with lead-based ammunition (e.g., Johansen et al. 2004), lead residues in game meat intended for human consumption (e.g., Andreotti et al. 2016), use of apex species as biomonitoring sentinels for lead exposure and effects (e.g., Mateo-Toma ´s et al. 2016), lead poisoning of critically endangered species (e.g., Bakker et al. 2016),
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| 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".