Comparing the Performance of IBIS and BulletTRAX‐3D Technology Using Bullets Fired Through 10 Consecutively Rifled Barrels*
Bibliographic record
Abstract
This study evaluates the abilities of the Integrated Ballistics Identification System (IBIS) and BulletTRAX-3D electronic imaging systems to identify bullets fired by the same weapon in a large database of images. Ten consecutively rifled handgun barrels were test fired to obtain reference sample and known match sample pairs for upload onto both bullet acquisition systems. Both copper-jacketed and lead bullets were uploaded, to account for variations in the manner in which markings are reproduced on the different metal compositions. Ranked correlation lists were examined and evaluated. For copper-jacketed bullet correlations, both IBIS and BulletTRAX-3D identified all reference samples to their known matches within the top 10 positions. For lead bullets, BulletTRAX-3D identified all reference samples to their known match in the top 10 positions while IBIS identified only 30%. For inter composition comparisons, BulletTRAX-3D was more successful than IBIS, identifying 100% of reference samples to their known match in the top 20 for copper-jacketed to lead comparisons and 90% for lead to copper-jacketed comparisons. These results suggest that BulletTRAX-3D is more effective than IBIS in the analysis of a wider range of bullet types and it was also found to produce images of superior quality.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".