Objective Identification of Bullets Based on 3D Pattern Matching and Line Counting Scores
Why this work is in the frame
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Bibliographic record
Abstract
In firearm identification, a firearm examiner looks at a pair of fired bullets or cartridge cases using a comparison microscope and determines from this visual analysis if they were both fired from the same firearm. In the particular case of fired bullets, the individual firearm signature takes the form of a striated pattern. Over the time, the firearm examiner’s community developed two distinct approaches for bullet identification: pattern matching and line counting. More recently, the emergence of technology enabling the capture of surface topographies down to a submicron depth resolution has been a catalyst for the field of computerized objective ballistic identification. Objectiveness is achieved through the statistical analysis of various scores of known matches and known nonmatches exhibit pair comparison, which in turn implies the capture of large quantities of bullets and cartridge cases topographies. The main goal of this study was to develop an objective identification method for bullets fired from conventionally rifled barrels, and to test this method on public and proprietary bullet 3D image datasets captured at different lateral resolutions. Two newly developed bullet identification scores, the Line Counting Score (LCS) and the Pattern Matching Score, computed on 3D topographies yielded perfect match versus nonmatch separation for three different sets used in the standard Hamby–Brundage Test. A similar analysis performed using a larger, more-realistic set, enabled us to define a discriminative line at a false match rate of 1/10[Formula: see text]000 on a 2D plot that shows both identification scores for matches and nonmatches. The LCS is shown to produce a better sensitivity than the standard consecutive matching striae criteria for the more-realistic dataset. A likelihood function was also computed from a linear combination of both scores, and a conservative approach based on extreme value theory is proposed to extrapolate this function in the score domain where nonmatch data are not available. This study also provides a better understanding of the limitations of studies that involve very few firearms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it