Elemental Characterization and Discrimination of Nontoxic Ammunition Using Scanning Electron Microscopy with Energy Dispersive X‐Ray Analysis and Principal Components Analysis
Bibliographic record
Abstract
Concerns over the toxic by-products produced by traditional ammunition have led to an increase in popularity of nontoxic ammunition. In this work, the chemical composition of six brands of nontoxic ammunition was investigated and compared to that of a road flare, which served as an environmental source with similar composition. Five rounds of each brand were fired while a further five were disassembled and the primer alone was fired. Particles collected from all samples, including the road flare, were analyzed by scanning electron microscopy with energy dispersive X-ray analysis. Common elements among the different ammunition brands included aluminum, potassium, silicon, calcium, and strontium. Spectra were then subjected to principal components analysis in which association of the primer to the intact ammunition sample was generally possible, with distinction among brands and from the road flare sample. Further, PCA loadings plots indicated the elements responsible for the association and discrimination observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".