Assessing the state of the art in Discrete Global Grid Systems: OGC criteria and present functionality
Bibliographic record
Abstract
The continuous growth of available geospatial data requires new methods for its integration, analysis, and visualization to be explored and implemented in software available to the geospatial community. Discrete Global Grid Systems (DGGS) are an emerging method for spatial data handling in the digital earth framework. DGGS are hierarchical data structures for discretizing the Earth’s surface that have seen considerable theoretical development over the last two decades. In this paper, four software implementations are reviewed, dggridR, H3, OpenEAGGR, and S2, to explore their potential applications in data modelling and GIS, as well as their performance. These software implementations were also evaluated against the recently published Open Geospatial Consortium (OGC) abstract specification. The results indicate great potential and versatility for utilizing such systems in geospatial analysis, if basic methods for converting and handling spatial features are further developed. The performance of these systems is shown to be highly scalable and operational with datasets of various sizes. Yet, it is demonstrated that the current software implementations generally fall short of fulfilling all of the OGC requirements or it was not possible to confirm their compliance. The assessment here identified that further enhancements, endorsement of OGC criteria, and their explicit acknowledgment within official documentation remain key research needs for the evaluated software packages. Further work developing operational DGGS that solve real world problems may promote greater community adoption and integration of DGGS data structures into commonly used geospatial platforms.
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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 | 0.028 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| 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".