BUILT HERITAGE DOCUMENTATION AND MANAGEMENT: AN INTEGRATED CONSERVATION APPROACH IN BAGAN
Bibliographic record
Abstract
Abstract. Good practices in heritage conservation are based on accurate information about conditions, materials, and transformation of built heritage sites. Therefore, heritage site documentation and its analysis are essential parts for their conservation. In addition, the devastating effects of recent catastrophic events in different geographical areas have highly affected cultural heritage places. Such areas include and are not limited to South Europe, South East Asia, and Central America. Within this framework, appropriate acquisition of information can effectively provide tools for the decision-making process and management. Heritage documentation is growing in innovation, providing dynamic opportunities for effectively responding to the alarming rate of destruction by natural events, conflicts, and negligence. In line with these considerations, a multidisciplinary team – including students and faculty members from Carleton University and Yangon Technological University, as well as staff from the Department of Archaeology, National Museum and Library (DoA) and professionals from the CyArk foundation – developed a coordinated strategy to document four temples in the site of Bagan (Myanmar). On-field work included capacity-building activities to train local emerging professionals in the heritage field (graduate and undergraduate students from the Yangon Technological University) and to increase the technical knowledge of the local DoA staff in the digital documentation field. Due to the short time of the on-field activity and the need to record several monuments, a variety of documentation techniques, including image and non-image based ones, were used. Afterwards, the information acquired during the fieldwork was processed to develop a solid base for the conservation and monitoring of the four documented temples. The relevance of developing this kind of documentation in Bagan is related to the vulnerability of the site, often affected by natural seismic events and flooding, as well as the lack of maintenance. Bagan provided an excellent case study to test the effectiveness of the proposed approach, to prevent and manage the damages of catastrophic events, and to support retrofitting actions. In order to test the flexibility of adopted methodology and workflow, temples with different features – in terms of architectural design, shape, and geometry – were selected. The goals of these documentation activities range from testing digital documentation workflows for the metric and visual recording of the site (reviewing strengths and limitations of particular recording techniques), to the definition of effective conditions assessment strategies.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".