Multispectral Imaging and Microscopic Analysis of a Medieval Runic Manuscript (AM 28 8vo)
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".