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Record W4366462811 · doi:10.1007/s13280-023-01863-y

Promoting enforcement of non-lead hunting ammunition regulations and compliance in Europe and North America

2023· article· en· W4366462811 on OpenAlexaff
Vernon G. Thomas, Niels Kanstrup

Bibliographic record

VenueAMBIO · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAmmunitionCompliance (psychology)Lead (geology)EnforcementBusinessPolitical scienceGeographyLawGeologyArchaeologyPsychology

Abstract

fetched live from OpenAlex

The direct regulation of lead ammunition in North America and Europe has been mainly for hunting in wetlands. Little support among hunters and the ammunition makers exists for further regulation despite suitable lead substitutes and much education about the known risks to wildlife and human health from ingested lead. In the absence of personnel to detect use of lead ammunition and enforce regulations, hunter compliance is low. Identification of non-lead ammunition using existing electronic technology and an international protocol on the identification of non-lead rifle bullets is proposed to aid enforcement. An explicit definition of the chemical composition of lead substitutes is required in European Union legislation together with a more enforceable distinction between 'possession during hunting' and 'ownership' of lead ammunition. A more transdisciplinary regulatory approach to transitioning to non-lead ammunition is advised. It comprises widespread public health advisories, setting a maximum allowable lead level in commercial game meats in EU legislation, and public communication that emphasizes the benefits of non-lead ammunition use to all categories of wildlife and the public perception of hunting, whether in North America or Europe.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.283
Teacher spread0.239 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2023
Admission routes1
Has abstractyes

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