Effect of muzzle gas on forward blood spatter from a gunshot: Experiments with a supersonic de Laval nozzle
Bibliographic record
Abstract
For bloodstain pattern analysis (BPA), interpreting statistically reliable data on a crime scene resulting from gunshots is a great challenge. This is due to various uncertainties, including blood rheology, hematocrit, coagulation, surrounding atmospheric conditions, victim's peculiarities, gun types, geometries, etc. In addition, muzzle (propellant) gases that follow the bullet may influence the aerodynamics of blood spatter in the cases of short-range shooting. We studied the muzzle gas effect on forward blood spatter. Muzzle gas can penetrate the wound channel and be ejected from the bullet exit hole affecting the forward blood spatter. Experiments with blood atomization by a gas flow issued from a supersonic de Laval converging–diverging nozzle are conducted. Defibrinated sheep blood was enclosed in a thin solid cylinder, which was filled by a supersonic air flow ejected from a de Laval nozzle, mimicking the muzzle gas flow through a wound channel. The mass flow rate of the supersonic air stream was varied by controlling the upstream chamber pressure. It was found that the number counts of the forward blood spatter from the muzzle gas blasting peaked at relatively shorter distances from the exit hole compared to the one that would be caused by a bullet. The effects of the muzzle gas and bullet could cause the formation of a bimodal spatter distribution on the floor behind the exit hole. These findings imply that atomization events owing to muzzle gas cause coarser atomization than that of a bullet, which could facilitate BPA in distinguishing certain homicides from staged suicides.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 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".