HBIM: A Tool for Enhancing the Diagnosis of Historical Buildings: The Case of St. George’s Memorial Anglican Church, Oshawa
Bibliographic record
Abstract
The primary objective of this research is to address the research gap in the conservation of heritage buildings in Canada by integrating Historical Building Information Modeling (HBIM) as a tool. The proposed study aims to develop an enhanced framework for the preservation of historical buildings through the utilization of HBIM and 3D-scanning technology. As a result, the research aims to generate a comprehensive database comprising various families of models while also incorporating strategies for point-cloud clustering data. The significance of this research lies in its potential to contribute to the conservation and restoration process of historical buildings. Currently, there are a lack of standardized approaches and comprehensive databases for accurately documenting and reproducing historical buildings. By integrating HBIM and 3D-scanning technology, this research will enable the creation of highly accurate three-dimensional virtual models, consisting of millions of points, which will serve as a comprehensive dataset for the restoration of heritage buildings. The findings of this research will benefit multiple stakeholders. Preservation architects, conservationists, and heritage professionals will gain a valuable tool for documenting and analyzing historical buildings with a high level of precision. The comprehensive database and framework proposed in this study will facilitate decision-making processes during the restoration and preservation phases, ensuring that the original architectural elements and materials are faithfully reproduced. Additionally, policymakers and governmental organizations involved in heritage conservation can use the outcomes of this research to establish standardized guidelines and regulations for the preservation of historical buildings in Canada. Ultimately, the broader community will benefit from the enhanced preservation efforts, as it will contribute to the cultural and historical identity of the nation, fostering a sense of pride and connection to the past.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".