An innovative way to use <scp>3D</scp> modeling on burnt bone to differentiate heat fractures from blunt and sharp force trauma
Bibliographic record
Abstract
Abstract Assessments of blunt and sharp force trauma in forensic research are frequently reliant on research with individual long bones. As a result, information on the interpretation of the trauma on irregular bones is limited in unburned bones and an even bigger discrepancy is found if the fracture relates to blunt/sharp force in cremated bone. This research strives to differentiate between traumatic fractures and heat fractures in flat and irregular bones. Five human calottes and five human hemipelves were exposed to either blunt or sharp force trauma and then all were incompletely cremated. One hundred and eighty fractures, representing a mixture of traumatic and heat fractures, were captured using a Keyence VHX‐2000 digital microscope and analysis was done in combination with 3D software, Geomagic Studio 2014 and Geomagic Design X (2016). With virtual reconstructions and reverse engineering facilitated by the software, we were able to discern fracture boundaries, slopes, and variances between fracture types. 3D representation provided the ability to differentiate peri‐mortem trauma from heat fractures based on curvature analysis of fracture walls. Evidence of trauma types (blunt versus sharp) were found to be distinguishable at their impact site based on this curvature examination; however, shallow, secondary or tertiary trauma fractures were difficult to discern from heat fractures. Blunt force trauma impact sites and sharp force trauma impact sites were easily identifiable; secondary trauma fractures were sometimes clearly noted but may be misinterpreted. Overall, deep trauma fractures and heat fractures can be discerned from one another using this technology. This article is categorized under: Forensic Anthropology > Taphonomic Changes and the Environment Forensic Anthropology > Trauma Analysis Forensic Chemistry and Trace Evidence > Fire Debris Analysis
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".