The Use of Reality Capture Technologies to Mediate Relocation Impacts: A Case Study at the Perrenoud Homestead Provincial Historic Resource, Alberta
Bibliographic record
Abstract
Relocation of buildings has been a common practice for centuries and is now frequently used as a means to preserve heritage structures in the face of various economic, social, and environmental risks. In relocating a heritage structure, documentation is of the utmost importance because of the adverse effects that relocation can have. Reality capture technologies provide a powerful tool for rapidly recording real-world phenomena in three-dimensions but have yet to be utilized for the documentation needs of relocation projects. This thesis provides a novel example of these technologies used for not only documentation of, but in an assessment of the impacts of relocation at the Perrenoud Homestead Provincial Historic Resource (PHR). During its disassembly, the Perrenoud Homestead was digitally documented using terrestrial LiDAR (laser scanning) and drone-based photogrammetry. The resulting datasets were then used to explore the impacts of relocation to the structural integrity of the site, through a three-part analysis of visual inspection, angular measurements, and change detection. A discussion was then posed about the consequences of the project on the commemorative integrity of the site, looking at dynamics of reality capture and the physical components of the PHR, as well as changes to the visitor experience and accessibility of this site. Overall, this thesis presents an example of the benefits of reality capture technologies to heritage relocation projects, and advocates for more incorporation of these methods for similar initiatives in the future.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".