Bibliographic record
Abstract
The University of Victoria (UVic) operates an Infrastructure-as-a-Service science cloud for Canadian researchers, and a WLCG T2 grid site for the ATLAS experiment at CERN. At first, these were two distinctly separate systems, but over time we have taken steps to migrate the T2 grid services to the cloud. This process has been significantly facilitated by basing our approach on Kubernetes, a versatile, robust, and very widely-adopted automation platform for orchestrating and managing containerized applications. Previous work exploited the batch capabilities of Kubernetes to run the computing jobs of the UVic ATLAS T2, and replace the conventional grid Computing Elements, by interfacing with the Harvester workload management system of the ATLAS experiment. However, the required functionality of a T2 site encompasses more than just batch computing. Likewise, the capabilities of Kubernetes extend far beyond running batch jobs, and include for example scheduling recurring tasks and hosting long-running externally-accessible services in a resilient way. We are now undertaking the more complex and challenging endeavour of adapting and migrating all remaining functions of the T2 site - such as APEL accounting and Squid caching proxies, but in particular the grid Storage Element - to cloud-native deployments on Kubernetes. We aim to enable fully comprehensive deployment of a complete ATLAS T2 site on a Kubernetes cluster via Helm charts, which will benefit the community by providing a streamlined and replicable way to install and configure an ATLAS site. We also describe our experience running a high-performance self-managed Kubernetes ATLAS T2 cluster at the scale of 8,000 CPU cores for the last 2 years, and compare with the conventional setup of grid services.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.025 |
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".