THE EVOLUTION OF MODELLING PRACTICES ON CANADA’S PARLIAMENT HILL: AN ANALYSIS OF THREE SIGNIFICANT HERITAGE BUILDING INFORMATION MODELS (HBIM)
Bibliographic record
Abstract
Abstract. In this paper, we explore the evolution of modelling practices used to develop three significant Heritage Building Information Models (HBIM) on Canada’s Parliament Hill National Historic Site – West Block, Centre Block, and The Library of Parliament. The unique scope, objective, and timeline for each model required an in-depth analysis to select the appropriate classification for Level of Detail (LOD) and Level of Accuracy (LOA). With each project, the refinement of modelling practices and workflows evolved, culminating in one of our most complex and challenging projects – the Library of Parliament BIM. The purpose of this paper is to share ideas and lessons learned for the intricate challenges that emerge when using LOD and LOA classifications including trade-offs between model performance, tolerances, and anticipated BIM use. In addition, we will evaluate how these decisions effected managing the digitization, data processing, data synthesis, and visualisation of the models.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".