Validation of the lead‐in method in a practical shooting scenario
Bibliographic record
Abstract
The ability to determine bullet trajectories after a shooting incident can allow investigators to reconstruct the locations of individuals and the sequence of events that took place. By using trajectory rods, investigators can be provided with an immediate visual estimate as to what the path of the projectile may have been. In certain instances, the use of the probing method with trajectory rods is not appropriate due to their being a single, thin target material, or no secondary bullet impact site. In these cases, other methods such as the lead-in or the ellipse method may be useful. Overall, the lead-in method has not been well studied in the application to practical scenarios, such as those including bullet impacts on vehicle metal surfaces. This study has explored the accuracy of the lead-in method when a bullet impacts a typical vehicle metal surface using three firearm calibers, three blind participants, and two non-blind participants. The results of this study have shown that each caliber has its own characteristic error curve. In general, it was found that the lower the impact angle, the less errors were made by the participants. As the impact angle increases, the measurement errors increased, due to the smaller lead-in area present. The errors were found to have a wide range, with some being as low as 1° and some being as high as 13.9°. Further, it was found there was no significant effect on the errors of blind versus non-blind participants.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".