Lead micro- and nanoparticles directly observed within gunshot wounds in hunted game meat
Bibliographic record
Abstract
Bullets, shot, and other projectiles from firearms can fragment inadvertently when they strike a target. The fragmentation process is concerning for hunting, where the projectiles are often lead-based, and the targets are animals that will likely be ingested by people and/or scavenging wildlife. Medical radiography (lab-based polychromatic X-ray imaging instruments routinely used in hospitals and for dental exams) has been the most widespread and accepted method to reveal these fragments within thick, hydrated tissue sections. It is also deployed at some food banks to screen packages of donated game meat for lead contamination in the form of projectile fragments. We present the first synchrotron-based X-ray images of rifle and shotgun wounds in biological tissue from hunted wild game animals, and contrast them against medical radiographs. Micro- and nanoscale fragments, undetectable in medical radiographs, were directly observed within tissue for the first time and conclusively identified as lead using X-ray absorption and emission spectroscopies. The mass of just those lead fragments that were below the detection limit of medical radiography was quantified and found to exceed levels set by the US Centers for Disease Control and Prevention for protection of human health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".