DIGITIZING AND DOCUMENTING HERITAGE FOR CONSERVATION, A CASE STUDY: CHIRIBIQUETE NATIONAL PARK ARCHAEOLOGICAL SITE
Bibliographic record
Abstract
Abstract. This paper presents the documentation carried out by the Carleton Immersive Media Studio (CIMS) in collaboration with the Colombian Institute of Anthropology and History (ICANH) to support the conservation of the archaeological pictographs in the UNESCO World Heritage Site of Chiribiquete National Park, Colombia; listed in 2018. This project entitled Preserving the past: preventive conservation on World Heritage Site Chiribiquete National Park and its buffer zone (Colombia) is funded by the U.S. Ambassadors Fund for Cultural Preservation (AFCP) and managed by the Fundación Erigaie. The dense rainforest landscape includes a mountain range with Tepuis inscribed with Paleoindian pictographs, painted and layered over time. The area was the site of the Colombian armed conflict that ended in 2016 and now remains remote and highly inaccessible. High-resolution 3D dense clouds and meshes of the painted Tepuis were created to record the pictographs with a high level of detail. This method of non-destructive investigation results in minimal impact on the biological environment of the site and on the uncontacted Indigenous communities who continue to inhabit the area, and the results will enable further remote investigation of the pictographs. Such tools demonstrated effectiveness while communicating the mass, scale of the site, colour, and texture of the pictographs at a high level of detail. The non-invasive nature of the immersive documentation is a powerful tool in the ongoing conservation and management of the site by mitigating the impact of tourism, by providing a remote method of sharing and experiencing the archaeological site.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".