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Record W2336090187 · doi:10.1007/s13280-016-0780-y

The transition to non-lead rifle ammunition in Denmark: National obligations and policy considerations

2016· article· en· W2336090187 on OpenAlexaff
Niels Kanstrup, Vernon G. Thomas, Oliver Krone, Carl Gremse

Bibliographic record

VenueAMBIO · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Guelph
Funders15. Juni Fonden
KeywordsAmmunitionLead (geology)WildlifeLead poisoningRifleOpposition (politics)BusinessEnvironmental protectionPolitical scienceGeographyLawEcologyPoliticsBiologyMedicine

Abstract

fetched live from OpenAlex

The issue of Denmark regulating use of lead-free rifle ammunition because of potential risks of lead exposure in wildlife and humans was examined from a scientific and objective policy perspective. The consequences of adopting or rejecting such regulation were identified. Denmark is obliged to examine this topic because of its national policy on lead reduction, its being a Party to the UN Bonn Convention on Migratory Species, and its role in protecting White-tailed Sea Eagles (Haliaeetus albicilla), a species prone to lead poisoning from lead ingestion. Lead-free bullets suited for deer hunting are available at comparable cost to lead bullets, and have been demonstrated to be as effective. National adoption of lead-free bullets would complete the Danish transition to lead-free ammunition use. It would reduce the risk of lead exposure to scavenging wildlife, and humans who might eat lead-contaminated wild game meat. Opposition from hunting organizations would be expected.

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)0.008
metaresearch head score (Gemma)0.007
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: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0070.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.289
Teacher spread0.267 · 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

Citations28
Published2016
Admission routes1
Has abstractyes

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