DORIC TEMPLE HBIM LIBRARY FOR CULTURAL HERITAGE MANAGEMENT
Bibliographic record
Abstract
Abstract. Heritage Building Information Modelling (HBIM) can be a valuable tool for the efficient management of cultural heritage. Adopting Building Information Modelling (BIM) for heritage architecture requires investing in training for modelling as-found elements, establishing standards for modelling, and developing accessible libraries of parametric assets. Parametric families of the Doric Order column and entablature were modelled in Autodesk Revit using standard measurements of the Doric Order, with parameters assigned to each element to remain adjustable and adapt to any as found project. These families were modelled with the ability to adjust all geometry to any point cloud with the intention of uploading the Revit files to an accessible online database, the Multimedia Inventory of Architectural Heritage (MIAH), developed by Carleton Immersive Media Studio (CIMS). The integration of these families into an online platform for any heritage professional to download and modify intends to ease the modelling process for future projects, as well as standardize the families used in future projects. To define the process of collecting data for as-found modelling, this paper outlines the documentation and data processing for two archaeological sites on the islands of Rhodes and Kos in Greece. To assist future HBIM library users in understanding the families, the process of converting point cloud to HBIM is demonstrated through the development of a parametric HBIM of the Temple of Hephaestus, located in Athens, Greece.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.220 | 0.147 |
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".