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Record W1705554751 · doi:10.1002/ar.23130

Impact: Development of a Radiological Mummy Database

2015· article· en· W1705554751 on OpenAlexafffund
Andrew J. Nelson, Andrew Wade

Bibliographic record

VenueThe Anatomical Record · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicPaleopathology and ancient diseases
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of CanadaOntario Council on Graduate Studies, Council of Ontario Universities
KeywordsContext (archaeology)The InternetScale (ratio)Computer scienceArchaeologyHistoryGeographyWorld Wide WebCartography

Abstract

fetched live from OpenAlex

The Internet Mummy Picture Archiving and Communication Technology (IMPACT) radiological and context database, is a large-scale, multi-institutional, collaborative research project devoted to the digital preservation and scientific study of mummified remains, and the mummification traditions that produced them, using non-destructive medical imaging technologies. Owing to the importance of non-destructive analyses to the study of mummified human remains, the IMPACT database, website, and wiki provide a basis for anthropological and palaeopathological investigations, grounded in the most current technological imaging and communication standards, accessible through any internet connection, and protected against rapidly changing media standards. Composed of paired online radiographic and contextual databases, the IMPACT project is intended to provide researchers with large-scale primary data samples for anthropological and palaeopathological investigations. IMPACT addresses the limitations of the case-study approach to mummified human remains and contributes to the development of standards of practice in imaging of mummified remains. Furthermore, IMPACT allows researchers a greater appreciation of, and engagement with, patterns of health and disease in ancient times as well as the variability present in the mummification traditions of ancient Egypt and other cultures that sought to preserve their dead for eternity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.114
GPT teacher head0.310
Teacher spread0.196 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations42
Published2015
Admission routes2
Has abstractyes

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