Heritage recording applications of high resolution 3D imaging
Bibliographic record
Abstract
Summary form only given. The National Research Council of Canada (NRC) has developed several high-resolution 3D imaging systems as well as data modeling and display software for heritage recording applications. Numerous pilot applications development projects have been undertaken in collaboration with several Canadian museums as well as with international partners in China, Italy, the USA, the UK, France and Israel. The systems have been used to scan archaeological site features, ethnographic collections, paintings, sculptures and architectural elements on historic buildings and the results used for a wide range of heritage recording applications including archival documentation, research, conservation, replication as well as interactive 3D VR Theatre and virtual museum Web applications. For example, in 1999 and again in 2001, in collaboration with one of NRC's industrial partners, Innovision 3D, The Canadian Foundation for the Preservation of Chinese Cultural and Historical Treasures and the State Administration of Cultural Heritage (SACH), it has been used in a pilot project to demonstrate the heritage recording applications of the 3D imaging technology in the Three Gorges area of China. The purpose of this presentation is to present an overview of the imaging systems and the heritage recording applications demonstrated to date.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.130 | 0.021 |
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".