A Multi-Agent System Using in Spatial Information Sharing on Web-Based GIS
Bibliographic record
Abstract
Web based geospatial information system (WebGIS) has great strengths in the geospatial information community. As the Internet becomes more and more accepted in society as a means to disseminate and gather information, the communication of geographic information over the Web using Web GIS will find its position the evolving medium. The traditional GIS are only used by the special occupation, and now many users want and are able to use the spatial data of GIS on the Web. However, there are two major problems in the Web GIS construct at present, such as the architecture of WebGIS and sharing of spatial information. This paper report related works on the strategy consideration of the multi-agent system using in sharing spatial information and solving architectures successfully based on the WebGIS service. The spatial database adjustment, server composition, XML definition document (ISO/TC211 and GML) are easy to materialize via the former methods, but the creation of spatial data converter is needed to devise within a new solution, which might transcend the concept of general GIS or WebGIS engines. Using the peer-to-peer architecture by the multi-agent system, the sharing spatial data is operated directly among the different systems by the requesting from client. The data can be transferred freely in the system.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".