Observations and 3D Analysis of Controlled Cast‐Off Stains
Bibliographic record
Abstract
Cast-off stains are common patterns found at crime scenes involving blood shedding events. However, the analysis and interpretation of cast-off patterns remains an area lacking tools for crime scene investigators. Analyzing cast-off patterns may allow investigators to interpret the area from where an object may have been swung and thus determine the approximate location of a suspect or victim. This study looked at the position and distribution of cast-off patterns and area of origin as a starting point for the development of a method to analyze cast-off patterns. Through a series of tests using a controlled cast-off rig (n = 10), it was observed that a Path Volume Envelope (PVE) may be identified where the swinging path is contained in a volume along with an area of exclusion. The calculated center, linear position of the PVE was found to have an average error of just over 3.2 cm when compared to the known object swing path position. The maximum deviation of the PVE to the known swing path was found to be 5.0 cm with a standard deviation of 1.4 cm. Additional studies are required to investigate the effects of partial cast-off stains, wielded object velocity, direction of swing, distance from the projected surface, and other factors. The observations and analysis from this study were seen to be predictable and repeatable and may provide a possible new method for investigators to interpret cast-off stains.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".