HERITAGE DOCUMENTATION AND DIGITAL PRESERVATION: THE USE OF CLOUD-BASED SERVICES FOR HERITAGE CONSERVATION (THE CASE OF ST. ALBERT RIVER LOTS)
Bibliographic record
Abstract
Abstract. Climate change has become, among countless pressures, a dominant threat to heritage places. It is critical to identify, analyse, assess, and mitigate immediate risks, and manage unforeseeable, unavoidable, and adverse impacts of climate on heritage values. There is an urgency in the heritage field to identify practical, efficient, and repeatable ways to document and monitor the condition of diverse heritage resources and develop climate adaptation strategies. Digital technologies, cloud computing, and digital preservation of heritage places can play a vital role in support of condition assessment, conservation planning, and sustainable management of heritage resources. This paper discusses a pilot project that experiments with the application of this idea on a selected case study, St. Albert River Lots in Alberta, Canada, and examines the challenges and opportunities of employing Amazon Web Services (AWS) and other Cloud-based applications. The project aimed to prepare a 3D model, as a foundation for recording current conditions and a tool for monitoring the impacts of climate change on heritage aspects and values in order to assist with the preparation of a detailed conservation management plan for the place in a digital format and contribute to the interpretive programming activities and raising public awareness.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".