Preparation of a Ytterbium‐tagged Gunshot Residue Standard for Quality Control in the Forensic Analysis of <scp>GSR</scp>
Bibliographic record
Abstract
Preparation of a ytterbium-tagged gunshot residue (GSR) reference standard for scanning electron microscopy and energy dispersive X-ray spectroscopic (SEM-EDS) microanalysis is reported. Two different chemical markers, ytterbium and neodymium, were evaluated by spiking the primers of 38 Special ammunition cartridges (no propellant, no projectile) and discharging them onto 12.7 mm diameter aluminum SEM pin stubs. Following SEM-EDS microanalysis, the majority of tri-component particles containing lead, barium, and antimony (PbBaSb) were successfully tagged with the chemical marker. Results demonstrate a primer spiked with 0.75% weight percent of ytterbium nitrate affords PbBaSb particles characteristic of GSR with a ytterbium inclusion efficiency of between 77% and 100%. Reproducibility of the method was verified, and durability of the ytterbium-tagged tri-component particles under repeated SEM-EDS analysis was also tested. The ytterbium-tagged PbBaSb particles impart synthetic traceability to a GSR reference standard and are suitable for analysis alongside case work samples, as a positive control for quality assurance purposes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".