From scientific research to digital reconstruction: integration of historical documentation and VR experiences in Gerri de la Sal Project
Bibliographic record
Abstract
There is a lack of bibliography concerning the specific methodology and limitations of historical data gathering to achieve high-quality and accurate productions of virtual reality (VR) and augmented reality (AR) technology applied to heritage dissemination. Most papers focus on the technical features and dissemination application of 3D and VR; the rigour of the research process specifically aimed at 3D reconstruction is usually not addressed, or taken into account by digitalization technicians. Although digitalization of heritage sites carried out by the same team or project in charge of its documentation mitigates the problem, several digitalization and public dissemination projects are conceived as a subsequent step, usually conducted by specialists or technicians not necessarily familiar with the heritage site’s features. This paper addresses this methodological need through the researcher’s experience in a multidisciplinary VR heritage project. The aim is to integrate historical research within the development of a VR museum within the Orígens UNESCO Global Geopark in the Catalan Pyrenees, Spain. Specifically, it explores the particular needs, challenges, and limitations of historical data gathering to accurately produce VR reconstructions of a cultural heritage site in Gerri de la Sal. The site involves studying a prehistoric salt production complex, a medieval monastery, and traditional salt-making processes. The experience has allowed the authors to propose a viable 3-step work plan to conduct research and documentation for VR dissemination. The result is a 7-minutes long VR documentary, to be displayed at the local museum of Gerri de la Sal, meeting the international VR reconstructions rigour guidelines. Thanks to successfully achieving this objective, the researchers have also addressed basic limitations of the proposed methodology and have proposed viable strategies to overcome them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".