Bibliographic record
Abstract
Geographic information (GI) is produced and used by wide range of scientific spheres. There is an obvious tendency to integrate data and information from various branches of scientific research and also across different languages. This paper is, among others, a brief overview and evaluation of existing standards for integration of metadata. Main focus is on the standard ISO 19115. This standard is the basics for metadata integration in INSPIRE (The Infrastructure for Spatial Information in Europe). Possibilities of metadata extensions and community profiles are also included. Spatial data are usually distributed over several existing systems (geographical information systems, database management systems or file systems). A lot of work has been related to metadata integration till now. However, we can find only a few examples of appropriate metadata integration in present GI platforms. Thus, related step and main focus of this research is to analyze the main GI platforms that are used across the world. The following platforms were analyzed: Bentley, ESRI, Intergraph and some other metadata software providers, like MICKA, GeoNetwork and METIS. First of all, we have to analyze all supported standards (including their versions, exchange formats, etc.) in the analyzed GI platform. Afterwards, there has to be an analysis of main characteristics of the platform (i.e. software, data structure, import and export of metadata, editing, querying, supported catalogue service, system control, users control, language support, portrayal and future work on this GI platform). Finally, it is necessary to define a way how to integrate metadata independently on the GI platform. This research has been supported by funding from project No. MSM0021622418 called Dynamic geovisualization in risk management and project No. T206030407 called Management of geographic information and knowledge.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".