A THEORETICAL FRAMEWORK FOR MULTI-SCALE DOCUMENTATION OFDECORATED SURFACE
Bibliographic record
Abstract
Abstract. Wall paintings are a unique case of decorated surfaces because of its direct relation to the supporting structure. This paper points out the importance of considering the conservation of wall paintings from its fourth-dimensionality–surface (2D), depth (3D), and time (4D) – and taking into account three different scales–surface, building, and territory. The relationship between these three scales becomes significant with a case study of three temples: Loka-hteik-pan, Myin-pya-gu, and Kubyauk-nge, in the context of Old Bagan (Myanmar), where more than 2,500 temples with valuable wall paintings exist. To that end, firstly, different documentation techniques and management methods are reviewed for each of the scales proposed; secondly, a multi-scale documentation project, mostly unexplored within the context of wall paintings, is developed using BIM and GIS. Ultimately, the case study in Old Bagan proposes a comprehensive methodology to document and manage wall paintings that belong to a large group of heritage assets considering its four-dimensionality at multiple scales, addressing the interoperability at a basic level between: (1) surface and building, (2) building and territory, and (3) surface and territory. The objective is to create data exchange among different platforms and users, generating a collaborative instrument that evolves with the participation of different specialists.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".