A Worldwide Study of Bullets Fired From 10 Consecutively Rifled 9<scp>MM RUGER</scp> Pistol Barrels—Analysis of Examiner Error Rate
Bibliographic record
Abstract
This technical note is an update on a continuing study, first designed and initiated by Brundage et al. over twenty years ago 1-4, which seeks to test the community of forensic firearms examiners’ ability to associate fired bullets with the barrels through which they passed. To date, 697 participants have utilized over 240 test sets consisting of bullets fired through 10 consecutively rifled RUGER P-85 pistol barrels. Here, we report on the results of the ongoing “10-barrel test” up until the point in time of writing this manuscript. To analyze the totality of data thus far collected, a Bayesian approach was selected. Posterior average examiner error rates are assigned assuming only vague prior information. Given the data found over the course of this diverse decades-long study, our most conservative value for average examiner error rate has a posterior mean of 0.053% with a 95% probability interval of [1.1 × 10−5%, 0.16%].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".