Towards a virtual hub approach for landscape assessment and multimedia ecomuseum using multitemporal-maps
Bibliographic record
Abstract
Abstract. Landscapes are dynamic entities, stretching and transforming across space and time, and need to be safeguarded as living places for the future, with interaction of human, social and economic dimensions. To have a comprehensive landscape evaluation several open data are needed, each one characterized by its own protocol, service interface, limiting or impeding this way interoperability and their integration. Indeed, nowadays the development of websites targeted to landscape assessment and touristic purposes requires many resources in terms of time, cost and IT skills to be implemented at different scales. For this reason these applications are limited to few cases mainly focusing on worldwide known touristic sites. The capability to spread the development of web-based multimedia virtual museum based on geospatial data relies for the future being on the possibility to discover the needed geo-spatial data through a single point of access in an homogenous way. In this paper the proposed innovative approach may facilitate the access to open data in a homogeneous way by means of specific components (the brokers) performing interoperability actions required to interconnect heterogeneous data sources. In the specific case study here analysed it has been implemented an interface to migrate a geo-swat chart based on local and regional geographic information into an user friendly Google Earth©-based infrastructure, integrating ancient cadastres and modern cartography, accessible by professionals and tourists via web and also via portable devices like tables and smartphones. The general aim of this work on the case study on the Lake of Como (Tremezzina municipality), is to boost the integration of assessment methodologies with digital geo-based technologies of map correlation for the multimedia ecomuseum system accessible via web. The developed WebGIS system integrates multi-scale and multi-temporal maps with different information (cultural, historical, landscape levels) represented by thematic icons allowing to transfer the richness of the landscape value to both tourists and professionals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".