Optical profilometry for forensic bloodstain imaging
Bibliographic record
Abstract
Understanding the physical, chemical and biological changes that occur during the drying of a bloodstain is important in many aspects of forensic science including bloodstain pattern analysis and time since deposition estimation. This research assesses the use of optical profilometry to analyze changes in the surface morphology of degrading bloodstains created using three different volumes (4, 11, and 20 μL) up to 4 weeks after deposition. We analyzed six surface characteristics, including surface average roughness, kurtosis, skewness, maximum height, number of cracks and pits, and height distributions from the topographical scans obtained from bloodstains. Full and partial optical profiles were obtained to examine long-term (minimum of 1.5-h intervals) and short-term (5-min intervals) changes. The majority of the changes in surface characteristics occurred within the first 35 min after bloodstain deposition, in agreement with current research in bloodstain drying. Optical profilometry is a nondestructive and efficient method to obtain surface profiles of bloodstains, and can be integrated easily into additional research workflows including but not limited to time since deposition estimation. Optical profilometry is a non-contact tool to scan bloodstains in ambient conditions Drying phases are observable in small drip bloodstains Significant surface morphology changes occur within 35 min after deposition.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".