ERIES: Engineering Research Infrastructures for European Synergies
Bibliographic record
Abstract
ERIES responds to the call INFRA-2021-SERV-01-07: Research infrastructure services advancing frontier knowledge with the overall objective of providing transnational access (TA) to advanced research infrastructures in the fields of structural, seismic, wind and geotechnical engineering. This project, together with the research infrastructure team assembled, provides access to leading experimental facilities that permit users to advance frontier knowledge and conduct curiosity-driven research towards: the reduction of losses and disruption due to these hazards; the management of their associated risk; and the development of innovative solutions to address them that will contribute to a greener and more sustainable society.To this end, ERIES offers TA to the best European experimental facilities in each field, with new and unique infrastructures available for the first time in this programme, along with the provision of key infrastructure in Canada. It integrates past infrastructure projects’ successful results and implementation, such as SERIES and SERA, and expands access capabilities to new communities and disciplines not yet focused on.It will provide authoritative input for diverse stakeholders, from Civil Protection agencies to the European seismic building code. It will help develop future standards for experimental techniques in earthquake, wind and geotechnical engineering. It will provide a platform from which European researchers can create innovative solutions and testbed applications of next-generation technologies.With 13 partners from 8 countries, ERIES builds an essential element toward reducing losses, managing risk, and overall a greener and more sustainable engineering future in Europe.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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; both teacher heads agree on what is shown here.
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".