AUGMENTED EXPERIENCE TO DISSEMINATE CULTURAL HERITAGE: HOUSE OF COMMONS WINDOWS, PARLIAMENT HILL NATIONAL HISTORIC SITE (CANADA)
Bibliographic record
Abstract
Abstract. The use of photogrammetry and terrestrial laser scanning for building information modelling (BIM) in the documentation and conservation of Cultural Heritage (CH) is now well established. By combining BIM with the latest visualization technologies, powerful, semi-immersive experiences can be developed to enhance the dissemination of CH. In semi-immersive experiences such as Augmented Reality (AR), digital content can be overlapped on to physical spaces, providing a new way to interact with both the physical space and the digital content. This paper discusses the translation of a digital object created using BIM, into a physical object and the utilisation of this physical object as a trigger for an AR experience. The case study looks at one of the neo- Gothic window frames from the House of Commons in the Centre Block of the Parliament Hill National Historic Site, in Ottawa, Canada. The window frame is one in a series that represents a Canadian province or territory with a stained glass feature that includes floral emblems and heraldic symbols from the respective provincial or territorial shield. The frame in this case study corresponds to the stained glass window of five provinces. Using the replica frame as a target, the user can select which stained glass windows they would like to view in the AR application. Through these combined technologies, we argue that CH can be revealed in a more interactive way and therefore more engaging manner – making even inaccessible architectural details readily available to the public.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".