A Framework for the Evaluation of Marine Spatial Data Infrastructures – Accompanied by International Case-Studies
Bibliographic record
Abstract
Abstract Germany is currently developing a marine data infrastructure addressing different topics such as 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 at many administrative levels, from which specifications and courses of action are derived. To set up a conceptual framework for the marine data infrastructure of Germany (MDI-DE), scientists at the Chair for Geodesy and Geoinformatics at the Rostock University are building a reference model, evaluating meta-information systems and developing models to support common workflows in marine applications. Evaluating how other countries built their marine spatial infrastructures is important to learn where obstacles 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 evaluating marine spatial data infrastructures (MSDI). This framework is then used to analyse and evaluate the efforts of Canada, Australia, and Ireland with respect to marine data infrastructures.
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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 | 0.044 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".