Modular Web-Based Atlas Information Systems
Bibliographic record
Abstract
Atlas information systems (AIS) present spatial information on predefined themes and localities in the form of maps and other representations, generally focusing on correct cartographic appearance and offering a certain degree of user interaction. This article introduces the concept of modular AIS, a concept that is essential for the development of an AIS with modern computer technology. The main advantages of a modular architecture are twofold: first, an AIS software framework based on a modular architecture allows for easy and rapid customization to a certain theme and locality; second, functional enhancements and new technologies can be easily integrated into a modular AIS in order to optimally present and analyse the data at hand. Web-based AIS can benefit from the concept of modularity at three different levels: (1) The AIS client can adjust its functionality and adapt to the available technology on the present computer platform. (2) The AIS server can build an AIS client with custom-tailored data and functionality in real time, depending on the user's access rights, needs, or expertise. (3) Distributed, modular data storage greatly simplifies the design, implementation and maintainance of an AIS by using a mediation system. To illustrate the concepts presented, we will discuss selected technical aspects (e.g., Web-based map viewer technology, client–server communication), and describe an exemplary Web-based AIS that extends the modular core architecture through specialized functionalities for the analysis of geophysical data. It is the authors’ hope that the ideas presented will provide an introduction to the technical concepts for designers and developers of similar Web-based atlas information systems.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".