Bibliographic record
Abstract
The emergence of highly accurate LiDAR (Light Detection and Ranging) technology has opened new avenues for engaging with and understanding cultural heritage sites. How do we convert buildings like Ontario Hall into editable 3D models to engage various stakeholders in a campus setting? This initiative developed from the goal of MobArch (Queen's Mobile Laboratory for the study of Architecture and the Built Environment) to use non-invasive visualization technologies to understand the social, developmental, and environmental impacts upon our built environment and architectural past. Current initiatives involving LiDAR for heritage conservation investigate hidden aspects of sites or address structural disrepair. The Ontario Hall project uniquely documents the state of a heritage building before structural concerns arise. This project creates an accurate digital record of Ontario Hall for future infrastructure initiatives and cultural preservation. In addition, this project tests the limits of LiDAR with 3D reconstruction in CAD (Computer Aided Design) software. This project used the Leica RTC360 3D Laser Scanner to create over 300 scans with a point cloud density of 6mm. The resulting cloud was optimized using Leica Cyclone Register 360 Plus software and was imported to Sketch Up where a 3D model was manually reconstructed based on the cloud data. The result of this project is a SKP file containing the 3D model, and the point cloud. The cloud is a highly accurate resource for future infrastructure projects. The 3D model contains room volumes, windows, staircases, and external architectural elements in a more edit-friendly software environment allowing diverse stakeholders to creatively reimagine Ontario Hall. The model can be integrated into archives, websites, games, and VR. Heritage architecture can contribute to the marketing appeal and visual identity of Queen’s. Moreover, awareness of community heritage improves social wellbeing and addresses the need to connect with our built history.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".