DIGITAL PRESERVATION OF THE QUON SANG LUNG LAUNDRY BUILDING, FORT MACLEOD, ALBERTA
Bibliographic record
Abstract
Abstract. This paper describes the results of an emergency recording and archiving of a historic structure in Southern Alberta and explores the lessons learned. Digital recording of the Quon Sang Lung Laundry building in Fort Macleod, Alberta, was a joint initiative between Alberta Culture and Tourism and the University of Calgary. The Quon Sang Lung Laundry was a boomtown-style wood structure situated in the Fort Macleod Provincial Historic Area, Alberta. Built in the mid-1800s, the structure was one of the four buildings comprising Fort Macleod’s Chinatown. Its association with Chinese immigration, settlement, and emergence of Chinese-owned businesses in early twentieth-century Alberta, made the Quon Sang Lung Laundry a unique and very significant historic resource. In recent years, a condition assessment of the structure indicated that the building was not safe and that the extent of the instability could lead to a sudden collapse. In response, Alberta Culture and Tourism engaged the Departments of Anthropology and Archaeology and Geomatics Engineering from the University of Calgary, to digitally preserve the laundry building. A complete survey including the laser scanning of all the remaining elements of the original structure, was undertaken. Through digital modeling, the work guarantees that a three-dimensional representation of the building is available for future use. This includes accurate 3D renders of the exterior and interior spaces and a collection of architectural drawings comprising floor plans, sections, and elevations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".