CONSERVING BUILT HERITAGE FOR POSTERITY: A CONSERVATION APPROACH IN BAGAN
Bibliographic record
Abstract
Abstract. The new age of digital technologies has led to a shift in conservation approaches when documenting historic places for posterity. The availability of these new technologies has provided tools for better decision-making with respect to the conservation of fragile built heritage. For sites vulnerable to seismic activity, such as Bagan, frequent catastrophic events have strengthened the need for proper documentation. A multidisciplinary team, comprised of students from Carleton University, students from Mandalay Technological University, and staff from the Department of Archaeology and Library of Bagan, conducted a documentation workshop using digital recording techniques. The team selected four Buddhist temples in the city of Bagan, Myanmar, as case studies for the documentation expedition. The strategy included using active and passive sensing techniques (Figure 1), which were used to assess the character-defining elements associated to the built heritage of the case studies. Furthermore, the strategy involved examining the state of conservation of the built heritage. Following the on-site documentation, the data collected was processed to give 2D and 3D representations of the documented sites. These representations serve as a record for future use in conservation. Additional objectives related to the documentation of the four sites were to understand the obstacles in conserving built heritage with respect to the ancient city of Bagan, to expand the technical knowledge of the local staff and the future professionals, and to examine the values and limitations of the recording techniques employed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".