CONCEPTS, MODELS AND IMPLEMENTATION OF THE MARINE SPATIAL DATA INFRASTRUCTURE IN GERMANY (MDI-DE)
Bibliographic record
Abstract
Abstract. In Germany currently the development of a marine data infrastructure takes place with the aim of merging information concerning the fields coastal engineering, hydrography and surveying, protection of the marine environment, maritime conservation, regional planning and coastal research. This undertaking is embedded in a series of regulations and developments on many administrative levels from which specifications and courses of action derive. To set up a conceptual framework for the marine data infrastructure (MDI-DE) scientists at the Professorship for Geodesy and Geoinformatics at Rostock University are building a reference model, evaluating meta-information systems and developing models to support common workflows in marine applications. The reference model for the marine spatial data infrastructure of Germany (MDI-DE) is the guideline for all developments inside this infrastructure. Because the undertaking is embedded in a series of regulations and developments this paper illustrates an approach on modelling a scenario for the Marine Strategy Framework Directive (MSFD) using the Unified Modelling Language (UML). Evaluating how other countries built their marine spatial infrastructures is of main importance, to learn where obstacles are and errors are likely to occur. To be able to look at other initiatives from a neutral point of view it is necessary to construct a framework for evaluation of marine spatial data infrastructures. Spatial data infrastructure assessment approaches were used as bases and were expanded to meet the requirements of the marine domain. As an international case-study this paper will look at Canada's Marine Geospatial Data Infrastructure (MGDI), COINAtlantic and GeoPortal.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".