A retail sampling approach to assess impact of geographic concentrations on probative value of comparative bullet lead analysis
Bibliographic record
Abstract
The probative value of comparative bullet lead analysis (CBLA), a now discontinued technique that was used by the Federal Bureau of Investigation for more than 30 years, has been hotly debated over the last several years. One issue that has received relatively little attention concerns the degree of geographic dispersion of bullets as they pass from manufacturers to retailers. Proponents and critics of CBLA alike agree that geographic distribution is such a major consideration, if not a predominant one, that it could significantly diminish, or completely erode, the probative value of a CBLA ‘match’ or, in some cases, even make a match counter-probative. The inattention to this issue to date appears to be a consequence of lack of data, rather than lack of importance. Until now, no datum concerning bullet distribution has been presented in the public domain, critically hampering the proper estimation of the probative value of a CBLA match. In this paper, we use manufacturer packing codes on boxes of bullets in retail outlets at four sites in the United States as a surrogate measure of bullet lead compositions to gauge local retail bullet distribution. Using a weighted average packing code match probability, we found very high degrees of geographic concentration of bullet packing codes. Although these findings can only offer a rough estimate of the degree of geographic concentration of actual chemical compositions of bullets, they are sufficient to establish that geographic concentration does, in fact, exist. Such a concentration would have a significant impact on the probative value of any claimed CBLA match.
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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 | 0.128 | 0.340 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".