Implementing Digital Documentation Techniques for Archaeological Artifacts to Develop a Virtual Exhibition: the Necropolis of Baley Collection
Bibliographic record
Abstract
Over the past decade, virtual reality has been quickly growing in popularity across disciplines including the field of archaeology and cultural heritage. Despite numerous artifacts being uncovered each year by archaeological excavations around the world, only a select few are displayed and recorded in museums while the rest remain hidden away in storage facilities. The creation of virtual reality museums provides a potential solution to this problem. This project aims to optimize a computational workflow for digitally documenting these artifacts and designing virtual museum spaces for them to be displayed online. This project focuses on a selection of the most representative artifacts that have been conserved and restored from the Necropolis of Baley collection of burial vessels from Bulgaria. The prehistoric settlement of Baley dates to the Bronze and Early Iron Age in the Middle and Lower Danube Basin and the collection thus far includes over 450 burial artifacts. Through the use of photogrammetry and 3D scanning, photorealistic 3D models will be created and used as the basis of a virtual exhibition to showcase this important collection to the public and scientific community that can easily be shared online. A comparison will be conducted of the 3D results from the photogrammetry and the 3D scanner to determine the optimal workflow for large scale documentation of archaeological artifacts. This project showcases the applications of integrating documentation techniques in an online environment in order to showcase important collections to the public in an interactive way to promote cultural heritage to the public that may otherwise be unavailable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".