PLANNING BY USING DIGITAL TECHNOLOGY IN THE RECONSTRUCTION OF CULTURAL HERITAGE SITES – A CASE STUDY OF QIONG-LIN SETTLEMENT IN KINMEN AREA
Bibliographic record
Abstract
Abstract. ICOMOS Florence Declaration in 2014, encourages an in-depth reflection on human values through cultural heritage and landscapes, which emphasizes the importance of historical heritage sites, in order to achieve the application of cultural heritage records through the public participation, sharing new technology platform and facilitation tools for knowledge diffusion, for instance. Nikos adopted digitized intangible cultural heritage within i-Treasures project to create a novel digital platform in 2016. Nowadays, the display platform developed based on geographic information system has been gradually accepted and widely used to distribute cultural heritage information, aiming to combine geography, time, events, issues, trends with the interactive maps to show the context of data changes from the consideration of planarity; for example, Burnaby City in Canada has cooperated with the Columbia University to create a navigation platform for guidance of tangible cultural heritage based on story maps in order to provide public recognition function. In this study, Qiong-Lin Settlement in Kinmen Area was taken as an example to illustrate the developing process of an overall planning framework for reappearing the glory of historic settlements of cultural heritage sites with digital technology, which included tangible and intangible cultural heritage preservation and transmission planning, community participation and digital navigation programs. The digital technology with the GIS-based digital platform can provide more diverse and interesting information while using an intuitive, graphical user story mapping interface. So that tangible cultural heritage can be effectively understood, interpreted and preserved with the value-added methods, and also intangible cultural heritage can be continuously transmitted to establish a complete system of cultural heritage preservation. The main contents include several navigation technologies, such as 3D laser scanning, UAV images, photogrammetry, panorama, audio/video, geographic information systems etc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".