LEVERAGING EXISTING HERITAGE DOCUMENTATION FOR ANIMATIONS: SENATE VIRTUAL TOUR
Bibliographic record
Abstract
Abstract. The use of digital documentation techniques has led to an increase in opportunities for using documentation data for valorization purposes, in addition to technical purposes. Likewise, building information models (BIMs) made from these data sets hold valuable information that can be as effective for public education as it is for rehabilitation. A BIM can reveal the elements of a building, as well as the different stages of a building over time. Valorizing this information increases the possibility for public engagement and interest in a heritage place. Digital data sets were leveraged by the Carleton Immersive Media Studio (CIMS) for parts of a virtual tour of the Senate of Canada. For the tour, workflows involving four different programs were explored to determine an efficient and effective way to leverage the existing documentation data to create informative and visually enticing animations for public dissemination: Autodesk Revit, Enscape, Autodesk 3ds Max, and Bentley Pointools. The explored workflows involve animations of point clouds, BIMs, and a combination of the two.
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 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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".