Paleoradiology in mummy studies: the Sulman mummy project.
Bibliographic record
Abstract
Can Assoc Radiol J 2004;55(4):228-34. Submitted Mar. 30, 2004 Accepted May 17, 2004 Mummies provide excellent material for research in the fields of bioarchaeology and the history of disease. Mummies reach across time and space to tell us about their lives and their cultures. Although many cultures have practiced mummification of human and animal remains, the term “mummy” generally brings Ancient Egypt to mind. The popular perception of the process of mummification is heavily shaped by the writings of Herodotus, but in fact there was a great deal of variation in Egyptian funeral rites. The first Egyptian mummies (ca. 5000 BC) were naturally desiccated in the desert sands. Artificial mummification developed to improve levels of preservation, thereby maintaining a form as lifelike as possible to allow the soul to survive in the afterlife. The variation in mummification methods tells us much about the mummy’s time period and the social status of the individual in life. The fact that these individuals have been preserved for many thousands of years allows us to retrieve information about health and disease, as well as about their daily lives and their funerary rituals. In this paper, we describe a previously unreported, ongoing and collaborative paleoradiologic project focused on an Egyptian mummy, involving researchers from the Department of Anthropology at the University of Western Ontario (UWO), St. Joseph’s Health Care, Robarts Research Institute and the National Research Council of Canada’s Virtual Environment Technologies Centre (VETC), London, Ont. This project mirrors the evolution of paleoradiology in mummy studies, from basic plain film images to the latest 3-dimensional (3D) reconstructions based on computed tomography (CT).
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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 | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".