A Digital Publication Prototype: The King’s Chamber
Bibliographic record
Abstract
Abstract. Remote sensing and 3D digitization technologies now generate rich heritage data streams that deepen our understanding of past civilizations. However, despite advances in web-based distribution platforms and efforts to establish standards — including the EU Study on quality in 3D digitisation — effectively sharing the synthesis, interpretation, and meaning derived from this data remains challenging. Existing scholarly platforms such as Omeka, Scalar, the Getty’s Quire, Pure 3D's Scholarly Editions, and Harvard's Digital Giza Project each approach this challenge differently — though these represent just a few examples. The King's Chamber is a digital publication prototype that integrates 3D datasets from photogrammetry and laser scanning with high-resolution 2D imagery, illustrations, archival photography, and detailed epigraphic analysis. This practice-led exploration examines how digital publications combining 2D and 3D data can complement traditional print publications, functioning as a case study to define community needs rather than being driven by technological capabilities alone. The project treats information organization and aggregation as vital documentation components. As 3D documentation becomes ubiquitous, a central question persists: what unique value can 3D publications offer beyond their 2D counterparts?
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".