Scenes from the Past: Multidetector CT of Egyptian Mummies of the Redpath Museum
Bibliographic record
Abstract
As a nondestructive method of historical and anthropologic inquiry, imaging has played an important role in mummy studies over the past several decades. Recent technologic advances have made multidetector computed tomography (CT) an especially useful means for deepening the present understanding of ancient cultures by examining preserved human remains. In April 2011, three ancient Egyptian human mummies from the Redpath Museum of McGill University were examined with 320-section multidetector CT as part of the IMPACT Radiological Mummy Database project headquartered at the University of Western Ontario. Whole-body scanning was performed with a section thickness of 0.5 mm and a peak voltage of 120 kVp, and the raw CT datasets were postprocessed by using smooth body and high-resolution bone convolution filters. Two of the mummies were scanned at different energy levels (80 and 135 keV). The high-resolution CT scans revealed the details of mummification and allowed observations about the socioeconomic and health status of the human subjects based on both the mummification technique used and the appearance of the remains, particularly the bones and teeth. The paleopathologic information obtained from the scans confirmed some findings in studies performed in the same mummies in the late 19th and 20th centuries. The CT scans also demonstrated a high degree of variability in Egyptian mortuary practice, variability that is not generally recognized in the literature. Unusual features that were observed included a relatively uncommon retained heart in mummy RM2718, retained lungs in a mummy from which the heart had been extracted (RM2720), and a cartonnage plaque placed over the left abdomen of a mummy that had been eviscerated transperineally (RM2717).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".