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Record W3176402588 · doi:10.3233/epl-201068

Promoting the Transition to Non-Lead Hunting Ammunition in the European Union Through Regulation and Policy Options

2021· article· en· W3176402588 on OpenAlexaff
Vernon G. Thomas, Niels Kanstrup, Deborah J. Pain

Bibliographic record

VenueEnvironmental Policy and Law · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Guelph
FundersAarhus Universitet
KeywordsAmmunitionEuropean unionBusinessEuropean commissionLead (geology)LegislationCommodityEnforcementInternational tradeNatural resource economicsCommerceLawPolitical scienceFinanceEconomics

Abstract

fetched live from OpenAlex

Regulation (EU) 2021/57, banning the use of lead gunshot in wetland hunting, and adoption of the proposed European Union (EU) restriction on lead ammunition use by civilians in other types of hunting and target shooting, would complete the transition to non-lead ammunition use in the EU and ensure major compliance among hunters and shooters. The transition is possible since non-lead substitutes for all types of shotgun and rifle ammunition are produced already by leading European manufacturers. To ensure ammunition non-toxicity, EU standards are needed for lead substitutes to accompany both existing and potential future lead ammunition restrictions. Meat from wild game birds and mammals is a large and important commodity in the EU. Setting a maximum lead level in all marketed game meats under Regulation (EC) 1881/2006, aided by mandatory food labelling, would add extra health protection to human consumers. This regulatory step would help ensure that all wild game destined for retail markets were taken with non-lead ammunition, would complement existing and proposed European Commission restrictions on lead hunting ammunition and aid monitoring and enforcement. Increased public awareness of the risks posed by lead from ammunition to the health of humans, wildlife, and the environment, and especially their associated externalized costs to society, would promote and facilitate the passage of regulation to protect human and environmental health from toxic lead ammunition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0020.004
Scholarly communication0.0120.007
Open science0.0040.005
Research integrity0.0300.005
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.019
GPT teacher head0.267
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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