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Record W4388173940 · doi:10.16995/dm.10359

Multispectral Imaging and Microscopic Analysis of a Medieval Runic Manuscript (AM 28 8vo)

2023· article· en· W4388173940 on OpenAlexvenueno aff
Paola Peratello

Bibliographic record

VenueDigital Medievalist · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Archaeological Studies
Canadian institutionsnot available
FundersUniversità degli Studi di Verona
KeywordsComputer scienceStyle (visual arts)FontFocus (optics)Object (grammar)Class (philosophy)ArtWorld Wide WebArt historyInformation retrievalArtificial intelligenceVisual arts

Abstract

fetched live from OpenAlex

In recent decades scholars started reaching out to advanced imaging technologies to reveal hidden text of manuscripts or to identify features that are undetectable to the naked eye. To this end, Copenhagen, Den Arnamagnæanske Samling, AM 28 8vo, also known as Codex Runicus, one of the most famous and intriguing Danish medieval manuscripts written entirely in medieval runes, underwent multispectral imaging (MSI) and microscopic analysis. AM 28 8vo has been studied, edited and digitized, but no in-depth analysis of material features like erasures, changing of inks, missing or faded portions of texts by means of digital-based methods exists yet. Along with three scribes who wrote the texts, AM 28 8vo also includes a marginal apparatus that carries valuable information on the history of its ownership and its reading by leading Danish philologists. Some inks of both the main text and marginalia are examined here combining MSI and microscopic analyses to understand possible correlations between those who wrote, edited and annotated the texts directly on the manuscript. Finally, this contribution reports on the results of such analyses, taking spectral reflectance as a valid guide in a first attempt of mapping scribal hands, and demonstrates how it can expand our understanding of the manuscript, its production, and its history.

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 score0.814
Threshold uncertainty score0.506

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.245
Teacher spread0.212 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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