Balancing Scales: Documenting a 17th Century Adobe Structure for its Holistic Assessment and Conservation using Multiple Scales of Level of Detail
Bibliographic record
Abstract
Abstract. Level of Detail is a critical parameter of cultural heritage documentation work, which describes the amount of information presented at a given geometrical scale of representation. Within the parameter of LOD, precision, completeness and accuracy constraints are used to control the quality of the recorded data. These parameters are therefore key constraints throughout the planning, acquisition, and processing workflow for documenting a cultural heritage site. These were the key concerns during the documentation of the Church of Santo Tomás de Aquino in Rondocan, Perú. This project was conducted through collaboration between the Carleton Immersive Media Studio, the Getty Conservation Institute, and the Dirección Desconcentrada de Cultura de Cusco, local branch of the Ministerio de Cultura de Perú in 2024, with the objective of producing a comprehensive set of digital assets for the conservation of the site. By controlling the precision, completeness and accuracy of data throughout the surveying process, involving geodetic surveying, laser scanning, and photogrammetry, a high baseline of the quality of the data was maintained throughout the project, and from there an appropriate Level of Detail was possible for each digital asset, including a solid model for structural analysis, ortho-corrected images, and architectural drawings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".