PROJECT ANQA: PRESENTING THE BUILT HERITAGE OF DAMASCUS, SYRIA THROUGH DIGITALLY-ASSISTED STORYTELLING
Bibliographic record
Abstract
Abstract. There is a growing interest in using new technology to create high-quality 3D recordings of heritage sites at potential risk of damage from conflict or natural disaster. Project Anqa is a multi-partner initiative to digitally document and present seven such at-risk heritage sites, all of which are located in Damascus, Syria. Through a training program, we enabled Syrian locals to collect a variety of data from all seven sites. With this data - a combination of photographs, laser-scan data and audio interviews - we present a web-application that provides researchers and the public a visually rich experience that showcases these at-risk sites. We term this approach “digitally-assisted storytelling.” Our goal is to raise awareness of the need to document and preserve at-risk heritage in the Middle East while providing local professionals in the region with the skills to carry out these tasks. Furthermore, Project Anqa aims to be an educational resource for both researchers and the public. By allowing all collected data to be downloaded at no charge through an open access platform, we encourage the transfer of knowledge and information while preserving the digital longevity of this endeavour.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".