INFORMATION MANAGEMENT SYSTEMS FOR MONITORING AND DOCUMENTING WORLD HERITAGE - THE SILK ROADS CHRIS
Bibliographic record
Abstract
Abstract. This paper discusses the application of Information Management Systems (IMS) for documenting and monitoring World Heritage (WH) properties. The application of IMS in WH can support all stakeholders involved in conservation, and management of cultural heritage by more easily inventorying, mining and exchanging information from multiple sources based on international standards. Moreover, IMS could assist in detecting damages and preparing management strategies to mitigate risks, and slowing down the deterioration of the integrity of WH properties. The case study of the Silk Roads Cultural Heritage Resource Information System (CHRIS), a Belgian Federal Science Policy Office funded project, illustrates the capabilities of IMS in the context of the nomination of the Central Asian Silk Roads on the WH List. This multi-lingual, web-based IMS will act as a collaborative platform allowing for the completion of improved transnational nomination dossiers and subsequent monitoring activities with all necessary baseline information to easily verify consistency and quality of the proposal. The Silk Roads CHRIS Geospatial Content Management System uses open source technologies and allows to georeference data from different scales and sources including data from field recording methods and combine it with historical and heritage features documented through various means such as textual descriptions, documents, photographs, 3D models or videos. Moreover, tailored maps can also be generated by overlaying a selection of available layers and then be exported to support the nomination dossier. Finally, by using this innovative information and decision support system, the State Parties and other interested stakeholders will have access to a complete nomination dossier and could therefore respond more effectively to hazards and disaster phenomena.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".