Byzantine Sigillography and RTI: Insights from the DigiByzSeal Project in Cologne
Bibliographic record
Abstract
This contribution discusses the application of Reflectance Transformation Imaging (RTI) in Byzantine sigillography, focusing on the experience of the DigiByzSeal project in Cologne. The project addresses the challenge of analyzing damaged or corroded Byzantine seals by employing RTI, an imaging technique that involves multiple captures with varying light positions in order to reveal epigraphic and iconographic features that are otherwise invisible.The paper also discusses the development of our RTI workflow, using an RTI Dome built at the Cologne Centre for eHumanities (CCeH), including capture preparation and determination of the optimal camera settings for each seal. Initially using darktable for tethered capturing, the project faced various issues and limitations, leading to the development of a custom capturing software. This software offers detailed control over camera configuration and streamlines the capture process, providing a user-friendly and efficient interface for capturing seals in a controlled environment. Further enhancements include the integration of Bluetooth connectivity to remotely control the RTI Dome, thereby fully automating the process. For the RTI processing part, RelightLab is presented as the software of choice, offering advantages over previously used tools in terms of user-friendliness and efficiency.Finally, it is discussed how the RTI images obtained have proved crucial for the analysis and interpretation of seals from the Robert Feind Collection, showing the potential of RTI for studying damaged artefacts and contributing to research on Byzantine sigillography.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".