Applications of 3D scanning in bloodstain pattern analysis
Bibliographic record
Abstract
3D terrestrial laser scanning has a variety of documented applications in numerous industries. With regards to forensic science and policing, 3D crime scene capture research has examined Road-Traffic Collision (RTC) investigations, stature estimation, ballistics and Bloodstain Pattern Analysis (BPA). Following a summary of these applications and an introduction to the scope of this PhD (chapter 1), this thesis illustrates what is generally encountered by scientists at “BPA scenes” (chapter 2), explores the accuracies and applicability of blood-drop trajectory software (chapter 3) and demonstrates the effectiveness of 3D crime scene modelling to jurors during BPA evidence presentation (chapter 4). Overall, this thesis concludes that 3D scanning for BPA could provide benefits in court during evidence presentation by expert witnesses to improve juror understanding. More commonly documented applications of 3D scanning for BPA have been for blood-drop trajectory software. However, a systematic review of the validation literature in this thesis found significant inaccuracies in documented experiments with limited and idealised experimental parameters. This thesis argues for the publication of any privately held data that validates the software in its current form, or development of the underlying software mechanics to improve reported accuracies in subsequent experiments. Validation must then be achieved with comprehensive experimental validation papers subjected to rigorous scientific peer-review. This thesis recommends a review by the Forensic Science Regulator to re-examine the literature basis for these software signposted from their Code of Practice and FSR-C-102, the Code of Practice and Conduct for Bloodstain Pattern Analysis. The publications within this thesis have positively contributed to forensic science and the field of BPA. Whilst causality cannot be confirmed, after chapter 2’s published recommendation in 2022 that BPA terminology should be reviewed, the Approved American National Standard for BPA terminology, first published in 2017, is now listed under review for a 2nd edition, at the time of this thesis’ submission in December 2023. Chapter 2, a quantification study of commonly encountered patterns, serves as a prompt for academics to direct their focus towards exploring lesser researched but commonly encountered patterns. Finally, the utility of 3D scanning for BPA evidence presentation, detailed in chapter 4, may encourage forensic scientists to explore further use of 3D visualisations when presenting complex spatial evidence at court to improve juror engagement and knowledge retention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.168 | 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 teacher head, 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".