The Dynafed data federator as a grid site storage element
Bibliographic record
Abstract
The Dynafed data federator is designed to present a dynamic and unified view of a distributed file repository. We describe our use of Dynafed to construct a production-ready WLCG storage element (SE) using existing grid storage endpoints as well as object storage. Dynafed is used as the primary SE for the Canadian distributed computing cloud systems for the Belle-II experiment, where we use it in production for reading input files. We have run up to 6,000 Belle-II jobs simultaneously on distributed cloud resources, requiring the transfer of approximately 60 TB of input data per day. Similarly, we have been using a Dynafed-based SE in pre-production testing for the ATLAS experiment. We will describe the configuration of Dynafed to make it suitable as a WLCG SE. In particular, we will highlight the improvements within Dynafed that make it possible to do checksum based file verification and 3rd-party file copy via WebDAV. We will also report on a new monitoring system and an automated system that collects storage information from all endpoints.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".