Introduction to the OGC Geodatacube Standard Working Group
Bibliographic record
Abstract
Over the past decade, a multitude of independent initiatives have developed solutions to answer the need for Analysis-Ready Data, in order to reduce the time and effort required in order to generate added value out of raw, heterogeneous data. These initiatives resulted in various GeoDataCube (GDCs) implementations, standards, data formats and best practices.Interoperability between the GDC solutions has so far not been a core concern but with the increasing amount of data served as GDCs as well as its increasing uptake, it is becoming essential to understand what exactly a GDC entails, how it was created, and how different GDCs can be used together consistently. This presentation shall focus on the OGC GeoDataCube Standards Working Group (SWG) which has recently been initiated to tackle these issues proposing viable solutions defined as a standard.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.024 |
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".