Delivering Geoscience Knowledge in Federal Systems: What Can the Old and New Worlds Learn from Each Other?
Bibliographic record
Abstract
Across the globe, geological communities are facing the same four challenges: put simply, how do we best make data discoverable, shareable, viewable and downloadable, so that the user also has access to consistent data at a national and continental level? The principle of managing scientific data and knowledge where it is generated and is best understood is well established in the science community. The distributed nature of most data sources means the complementary delivery mechanism of web map services has become equally prevalent in the spatial data community. Together these two factors are driving a world-wide revolution in the way spatial geoscience information is being disseminated to its users. The outcome is that data are being managed and delivered from multiple component sources - a federated system - ie the individual states within a union. These systems exist in the USA, in Canada, in Australia, and progressively, also in Europe, where the European Union can be regarded as a federal analogue, and where new regulation is placing the force of law behind spatial data infrastructures. In these "systems" addressing the four challenges are however, far from simple. To address them means finding solutions to adequate but workable metadata description, data specifications which encompass the richness of the data but deliver continuity, web map interfaces which allow flexible access but are easy to use, and last but not least intellectual property rules that protect the originator but provide the data the users need. The models for collaboration emerging in each of the federated systems are moving towards consensus on a global digital integration framework in the geosciences. We draw on the rich experiences in North America and Europe, and explore the way the challenges have been articulated and addressed with a strong emphasis on gaining future benefit by sharing the lessons learned.
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 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 | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.030 | 0.072 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".