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Record W4293105112 · doi:10.1371/journal.pone.0271987

Fragmentation of hunting bullets observed with synchrotron radiation: Lighting up the source of a lesser-known lead exposure pathway

2022· article· en· W4293105112 on OpenAlexafffund
Adam F. G. Leontowich, Arash Panahifar, Ryan Ostrowski

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsRoyal University HospitalUniversity of SaskatchewanCanadian Light Source (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of SaskatchewanCanadian Light Source
KeywordsFragmentation (computing)Synchrotron radiationSynchrotronLead poisoningPhysicsMaterials scienceOpticsBiologyEcology

Abstract

fetched live from OpenAlex

Bullet fragments have been previously observed in the remains and edible portions of big game animals that were harvested using rifles. The fragmentation issue has attracted attention because traditional hunting bullets are more than 70% lead, which is toxic to humans and scavengers in the ecosystem. We prepared gunshot wounds in ballistic gelatin blocks, and then applied synchrotron X-ray imaging technology to the bullet fragmentation process for the first time. The K edge subtraction (KES) imaging method allowed a clear separation of lead in an image from false positives, including the other major bullet component, copper, and non-lead objects such as bone fragments. The superior brightness of synchrotron radiation was also harnessed to resolve thousands of embedded sub-10 μm fragments, a size range not previously observed using commonly applied X-ray imaging modalities. The results challenge the current understanding of the maximum extent that fragments may be distributed, and the effectiveness of imaging methods used to screen wild game donations at food banks for lead bullet fragments.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.958

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 studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.211
Teacher spread0.151 · 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 designQualitative
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

Citations26
Published2022
Admission routes2
Has abstractyes

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