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Record W1965599725 · doi:10.1145/2686873

The Dream and the Cross

2015· article· en· W1965599725 on OpenAlexaff
Chiara Leoni, Marco Callieri, Matteo Dellepiane, Daniel Paul O’Donnell, Roberto Rosselli Del Turco, Roberto Scopigno

Bibliographic record

VenueJournal on Computing and Cultural Heritage · 2015
Typearticle
Languageen
FieldComputer Science
TopicVideo Analysis and Summarization
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsDigitizationDreamComputer scienceXMLContext (archaeology)PoetryTranscription (linguistics)World Wide WebMultimediaArtLiteratureHistoryLinguisticsTelecommunications

Abstract

fetched live from OpenAlex

The Dream of the Rood is one of the earliest Christian poems in Old English and an example of the genre of dream poetry. While a complete text can be found in the 10th-century “Vercelli Book,” the poem is considerably older, and its oldest occurrence is carved (in runes) on the 7- to 8th-century Ruthwell Stone Cross. In this article, we present the work done in the framework of the “Visionary Cross” project, starting from the digitization of the Ruthwell Cross to the creation of a web-based digital edition of The Dream of the Rood , as it is carved on the Cross. The 3D data has been collected and processed with the explicit aim of creating a multimedia framework able to present the highly detailed digital model acquired with 3D scanning technology, together with the transcription and translation of the runes that can be found on its surface. The textual and spatial information are linked through a system of bidirectional links called Spots, which allow the users to navigate freely over the multimedia content, keeping the 3D and textual data synchronized. The present work discusses the different issues that arose during the work, from digitization and processing to the design of a tool for the integration of three-dimensional content in the context of the presentation on the web platform of heterogeneous multimedia data. We end with the difficulties involved in the creation of an XML encoding that could account for the necessities of the visualization system but remain within the scholarly encoding standards of the relevant disciplinary community.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.019
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.014
Scholarly communication0.0100.008
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0190.002

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 head0.021
GPT teacher head0.283
Teacher spread0.262 · 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 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

Citations5
Published2015
Admission routes1
Has abstractyes

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