Metadata Recommendations for Geodetic data: GNSS
Bibliographic record
Abstract
Data publications with digital object identifiers (DOI) are key supporting elements for Findable, Accessible, Interoperable and Reusable (FAIR) data (Wilkinson et al. 2016). DOIs were originally developed to provide permanent access to static datasets described in scholarly literature. Currently, they are increasingly being used for both static and dynamic data. DOIs are providing globally unique, persistent and resolvable references of various types of sources (data, software, samples, equipment) and means of rewarding the originators and institutions (through citation). This is important for geodesy, as researchers rely on observational data and operational aspects. Geodetic equipment, observational data and results require a documented mechanism for citation, scientific recognition and reward that can be provided by assigning a DOI. To address these challenges and identify opportunities for improved coordination and advocacy within the geodetic community, GGOS established a Working Group on 'Digital Object Identifiers (DOIs) for Geodetic Datasets' in 2019, transforming it into a GGOS Committee in 2023. This Committee is designated to establish best practices and advocate for the consistent implementation of DOIs across all IAG Services and in the greater geodetic community. This document presents the GGOS DOI Committee's first set of recommendations for DOI metadata for geodetic data. These recommendations have been developed for GNSS data and include specific guidance on different GNSS data products. However, many sections of this document are not specific to GNSS data and may also be applicable to other geodetic data.
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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 | 0.030 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.049 | 0.082 |
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".