INTEGRATING HETEROGENEOUS DATASETS IN HBIMOF DECORATED SURFACES
Bibliographic record
Abstract
Abstract. There are multiple conservation challenges related to decorated surfaces, the majority are intimately linked to its documentation. This paper draws on wall paintings as a representative of decorated surfaces, arguing the importance of considering its fourthdimensionality – space and time – in its conservation and documentation. To that end, we propose the use of Building Information Model (BIM) as a platform to consolidate this approach together with various documentation techniques used for the conservation and management of wall paintings. This paper exemplifies this method with a case study of Myin-pya-gu Temple in Old Bagan (Myanmar); firstly, reviewing the different techniques used to document the temple and wall painting (photography, photogrammetry, laser scanning, reflectance transformation imaging (RTI); and secondly, discussing the data integration within a BIM environment. This position proposes a transition from a two-dimensional to a four-dimensional approach in wall painting conservation, potentially opening up possibilities of documentation, monitoring, simulation, or dissemination. Ultimately, the case study of Myin-pya-gu has the objective to introduce the use of HBIM as a platform for consolidating the documentation of decorated surfaces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".