Effect of muzzle gases on forward spatter of viscoelastic blood in close-range shooting
Bibliographic record
Abstract
Because bloodstain patterns resulting from close-range shooting are intrinsically different from those of long-range shooting, an accurate interpretation and delineation of these phenomena are essential in forensic science and crime scene analysis. Such a delineation would be helpful, for example, to distinguish whether a suicide or a homicide had happened. If the shooting was from a long-range (most likely a homicide), muzzle gases would not be able to influence blood spatter ejected from a victim. However, in the case of a close-range shooting, muzzle gases would greatly influence blood spatter. Herein, the effect of the muzzle gases on bloodstain patterns is studied. A de Laval nozzle is used to mimic an issue of supersonic “muzzle” gas from a gun barrel. The supersonic gas flow passes through a cylinder containing defibrinated sheep blood, which is blown off and atomized into numerous drops. These drops fly away and settle onto the floor or onto vertical walls at various distances from the cylinder exit. Viscoelasticity of the defibrinated sheep blood is enhanced by adding Xanthan to model different states of blood corresponding to different conditions. An impact of a vertically-released single drop onto an inclined substrate was also studied to elucidate splashing regimes/criteria of the blood drops of different viscoelasticity levels. We found that stronger elastic forces facilitated formation of bloodstains with higher ellipticity at a higher impact angles.
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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 | 0.000 | 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 | 0.000 |
| 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".