Geoheritage to Support Heritage Authorities: Research Case Studies on Maya Archaeological Sites
Bibliographic record
Abstract
Abstract Since the adoption of the World Heritage Convention (1972), modern technologies have significantly changed the way our society behaves and operates, with an increased demand for energy, fast and reliable communications, etc. Some modern technologies might contribute to negative impacts on heritage sites, e.g. through climate change and/or excessive tourism; however, modern digital technologies can also be extremely beneficial for heritage activities. In this paper, we focus on how modern digital geo-science and geo-technology can support heritage authorities’ daily work. We introduce herein the concept of digital Geoheritage, which can help heritage authorities to discover and understand the enormous benefits that geomatics can provide for their daily heritage activities. This research case, implemented through an interdisciplinary scientific approach, originally aimed to support the preservation, restoration and management of a cultural heritage site; however, it was later expanded to also support archaeological research, stability risk assessment, planning, design, education, dissemination and promotion. The use of digital geo-sciences for the benefit of the local Maya communities living around a heritage site is also illustrated. Our objective, within the current book, was to present a paper that is oriented toward heritage authorities, and, therefore, technical language has been avoided.
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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 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".