An innovative use of gaming technology for the presentation of stratigraphic information : a presentation of the Middle Palaeolithic deposits atv, Belgium
Bibliographic record
Abstract
The DigiArt project is currently in its final year. The main aim of the project is to improve the process of mass 3D digitisation for the cultural heritage sector. This objective includes the creation of a range of software solutions and commercially low-cost hardware to make the process of virtual curation and virtual visits for the public more democratic and more user friendly. These tools are meant for the curators of cultural heritage, be them archaeologists, anthropologists, museum curators or private people with interesting collections, to author dynamic scenarios into 3D cultural worlds with their heritage objects as the objects for composing the stories the public will be told. The project is unique in its consortium partners who are archaeologists, anthropologists, electrical, mechanical, optical and software engineers. The convergence of their ideas means that the aims of the project are driven by the cultural heritage workers. In the project, engineers have been working on finding a balance between capturing large scale sites, data accuracy and visual accuracy. Although the project is still ongoing, the culmination of the innovations made in the project will be the landscape for new immersive experiences to remote and onsite visitors. Although the definition of visitor in this project is considered the general public, as archaeologists and anthropologists we see potential beyond this stakeholder here. The ‘Story Telling Engine’ software package that is being created as part of this project can easily be adopted for providing more informative and more immersive ways of disseminating site information to the scientific community. The ease of a drag and drop feature to add 3D models of whole archaeological sites or specific stratigraphic sections and associated objects makes for a user-friendly tool. In demonstration of DigiAr’s “Story Telling Engine” and its usefulness in academic dissemination, we will present the stratigraphy of Scladina Cave (Belgium). This site has been subject to substantial analyses to further our understanding of its sedimentation processes. Thanks to this new user friendly tool, all stratigraphic records can be easily integrated into a high detailed 3D model of the cave. This system allows archaeologists to follow the evolution of the excavation and to reposition all the discoveries in situ.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
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".