Identifying Postmortem Microstructural Change to Skeletal and Dental Tissues using Backscattered Electron Imaging
Bibliographic record
Abstract
A number of papers have been published over a 100 year period describing postmortem microstructural change to bone and teeth in humans and other mammals. Much of the work is descriptive and has used a number of microscopic methods, which introduce changes during preparation, and are limited by the resolving power of that technique. Backscattered electron imaging in a scanning electron microscope (BSE/SEM) has been used successfully applied to on normal skeletal tissues and is an excellent method to document postmortem changes to bone and tooth microstructure. In forensic science, archaeology, and paleontology there is a collective interest in understanding early death history and subsequent treatment and deposition of the body. To this end the main microstructural changes are provided as a means of identification, and practical suggestions to circumvent misinterpretation due to artifacts created by employing the BSE imaging method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".