The role of computed axial tomography in the study of the mummies of Akhmim, Egypt
Bibliographic record
Abstract
For more than a quarter century, computed axial tomography (CT) has given Egyptologists an increasingly sophisticated, non-invasive means of examining the interior of mummified bodies. What has been lacking from mummy studies to date is a regional focus confining itself to a single, defined population which makes its comparisons within narrowly defined limits of time and space. A study of Akhmimic mummies, hundreds of which entered the museum collections of Europe, America and elsewhere late in the 19th century, promises to greatly benefit the study of Egyptian mummification generally while gathering specific data on Akhmim’s priestly population of the Ptolemaic period (332-30 BC). Recent CT examination of two female mummies from Akhmim has underscored the importance of considering features other than those on the traditional list of mummy contents. While amulets, visceral packets, and linen wadding have been noted for years, it is clear that they existed side by side with objects that, while difficult to classify, were equally deliberate and significant. The Akhmim Studies Consortium has been established to increase our knowledge of these poorly understood aspects of the embalming process as it existed at Akhmim and in surrounding locale
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".