The European Plate Observing System and the Arctic
Bibliographic record
Abstract
The European Plate Observing System (EPOS) aims to integrate existing infrastructures in the solid earth sciences into a single infrastructure, enabling earth scientists across Europe to combine, model, and interpret multidisciplinary datasets at different time and length scales. In particular, a primary objective is to integrate existing research infrastructures within the fields of seismology, geodesy, geophysics, geology, rock physics, and volcanology at a pan-European level. The added value of such integration is not visible through individual analyses of data from each research infrastructure; it needs to be understood in a long-term perspective that includes the time when changes implied by current scientific research results are fully realized and their societal impacts have become clear. EPOS is now entering its implementation phase following a four-year preparatory phase during which 18 member countries in Europe contributed more than 250 research infrastructures to the building of this pan-European vision. The Arctic covers a significant portion of the European plate and therefore plays an important part in research on the solid earth in Europe. However, the work environment in the Arctic is challenging. First, most of the European Plate boundary in the Arctic is offshore, and hence, sub-sea networks must be built for solid earth observation. Second, ice covers the Arctic Ocean where the European Plate boundary crosses through the Gakkel Ridge, so innovative technologies are needed to monitor solid earth deformation. Therefore, research collaboration with other disciplines such as physical oceanography, marine acoustics, and geo-biology is necessary. The establishment of efficient research infrastructures suitable for these challenging conditions is essential both to reduce costs and to stimulate multidisciplinary research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".