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TUTANKHAMEN’S TWO TOMBS: REPLICA CREATION AND THE PRESERVATION OF OUR CULTURAL HERITAGE IN THE DIGITAL AGE

2019· article· en· W2944153573 on OpenAlexaff
Lori Wong, Mario Santana Quintero

Bibliographic record

Venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2019
Typearticle
Languageen
FieldComputer Science
TopicImage Processing and 3D Reconstruction
Canadian institutionsCarleton University
Fundersnot available
KeywordsCultural heritageReplicaArchaeologyDigitizationHistoryPaintingComputer scienceArt historyTelecommunications

Abstract

fetched live from OpenAlex

Abstract. There are two tombs of Tutankhamen both located in Luxor, Egypt: one in the Valley of the Kings, excavated into the Theban bedrock and decorated with wall paintings, dating from 1323 BCE; the other, installed 3 km away, opened in April 2014 and is considered to be an ‘exact facsimile’ of the original tomb. Tutankhamen’s tomb is just one example of a cultural heritage site that has been replicated. This list is steadily growing as replicas are finding renewed favour fuelled by technological advancements in three-dimensional recording, capture and fabrication which has enabled the production of highly accurate replicas in both physical and virtual form. Comparisons drawn between the two tombs of Tutankhamen—the original and the replica—aim to highlight the differing approaches of parallel preservation projects and intends to prompt questions surrounding the commissioning and use of replicas in the cultural heritage field and the growing role that 3D digital technology is playing in the preservation/conservation sector. Sites and cultural heritage organization are scrambling to be part of the 3D digital revolution. But, has the transition to 3D and the revival in replicas happened too quickly and at the expense of a site’s other conservation needs? Is technology being used in the service of conservation and preservation or is it the other way around? How can those working with heritage balance the benefit of 3D technology with the overall conservation needs for a site? Using the example of Tutankhamen’s two tombs this paper aims to provoke discussion on these topics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.009
Scholarly communication0.0080.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.014
GPT teacher head0.261
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations15
Published2019
Admission routes1
Has abstractyes

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