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Record W3209585134 · doi:10.4081/jbr.2005.10086

The role of computed axial tomography in the study of the mummies of Akhmim, Egypt

2005· article· en· W3209585134 on OpenAlexaboutno aff
Jonathan P. Elias, Carter Lupton

Bibliographic record

VenueJournal of Biological Research - Bollettino della Società Italiana di Biologia Sperimentale · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicPaleopathology and ancient diseases
Canadian institutionsnot available
Fundersnot available
KeywordsEmbalmingComputed tomographyPopulationAncient historyAncient egyptEgyptologyQuarter (Canadian coin)Focus (optics)ArchaeologyArtHistoryGeographyMedicineDemographyRadiologySociology

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.334
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2005
Admission routes1
Has abstractyes

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