IRIS – providing a nationally accessible infrastructure for UK science
Bibliographic record
Abstract
In many countries around the world, the development of national infrastructures for science either has been implemented or are under serious consideration by governments and funding bodies. Current examples include ARDC in Australia, CANARIE in Canada and MTA Cloud in Hungary. These infrastructures provide access to compute and storage to a wide swathe of user communities and represent a collaboration between users, providers and, in some cases, industry to maximise the impact of the investments made. The UK has embarked on a project called IRIS to develop a sustainable e-infrastructure based on the needs of a diverse set of communities. Building on the success of the UK component of the WLCG and the innovations made, a number of research institutes and universities are working with several research groups to co-design an infrastructure, including support services, which take this to a level applicable to a wider use base. We present the preparatory work leading to the definition of this infrastructure, showing the wide variety of use cases which require to be supported. This leads us to a definition of the hardware and interface requirements needed to meet this diverse set of criteria, and the support posts identified in order to make best use of this facility and sustain it into the future.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".