The Accuracy and Repeatability of Reconstructing Single Bullet Impacts Using the 2D Ellipse Method
Bibliographic record
Abstract
When a bullet punctures a surface, it leaves behind a bullet impact, which can be analyzed in order to determine the origin and trajectory path from which a bullet was discharged using many different scientific methods to reconstruct a shooting scene. The purpose of the research was to test the accuracy and repeatability of reconstructing the impact angle of single bullet impacts using the ellipse method. The research was conducted by firing various calibers of ammunition into drywall panels positioned at varying angles of incidence from 88° until ricochet occurred. This was done in order to determine which caliber type and angle of incidence are most accurate and precise for reconstructing a shooting scene. The study examined four caliber types fired into drywall panels, with 5 repeated shots for each of the 11 angles of incidence (n = 220). Furthermore, 31 participants partook in the study to estimate a bullet impact angle of incidence utilizing the ellipse method. The results show that the best performance (accuracy and repeatability) is seen with the measurements of the 0.45 caliber ammunition. When angle of incidences is low (<64°), the performance (accuracy and repeatability) was seen to be better in all caliber ammunitions. Overall, the data provided for single bullet impacts deposited in drywall show that the ellipse method is useful in providing measurements for most crime scene reconstruction purposes and has also demonstrated that results vary depending on the type of ammunition, firearm, and angle of incidence being examined.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".