Bone Diagenesis and its Implication for Disease Diagnosis: The Relevance of Bone Microstructure Analysis for the Study of Past Human Remains
Bibliographic record
Abstract
When bone is exposed to the burial environment it may experience structural changes induced by multiple agents. The study of postmortem alterations is important to differentiate decomposition phenomena from normal physiological processes or pathological lesions, as well as to assess bone tissue quality. Microscopy is of great utility to evaluate the integrity of bone microstructure and it provides significant data on long-term bone decomposition. A total of 18 human bone sections (eight archeological and ten retrieved from an identified skeletal collection) were selected for analysis under plane light and polarized light. The aim of this exploratory study was to analyze the impact of diagenesis and taphonomy on the bone microstructure, as well as on the differential diagnosis of pathological conditions. The results showed that the microscopy approach to bone tissues contributed materially as an aid in the detailed description of the main diagenetic changes observed. It showed that gross inspection does not provide a realistic assessment of bone tissue preservation, which can impact in the characterization of lesions present and subsequent disease diagnosis. Therefore, researchers should continue to consider the application of histological techniques if the aim is to comprehend tissue integrity and its association with decomposition or disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".