Toxicological Analysis of Drugs in Human Mummified Bodies andProposed Guidelines
Bibliographic record
Abstract
From palaeopathology to forensic taphonomy, mummified human bodies constitute biological archives of paramount importance. Toxicology analysis of endobiotics and xenobiotics has already shown value to archaeological mummies research with detecting heavy metals, sedative-hypnotic drugs, and stimulants. Thanks to the large window of drug detection in hair and nails, the information from such studies has increased the scientific community's knowledge regarding past populations' lifestyles. Still, few bibliographic references exist regarding toxicology reports in mummified bodies from forensic settings. Here, the authors aim to draw attention to the valuable contribution of toxicology analysis, taking into account previously conducted studies and their findings. Given that sample collection on mummified bodies from forensic scenarios may not always happen in laboratories or autopsy rooms, the authors also suggest guidelines for in situ sampling of forensic mummies. It is expected that the present technical note will encourage experts to perform toxicology analysis in mummified bodies and publish their case reports more often.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".