Bibliographic record
Abstract
Belle II has started the Phase 3 data taking with a fully equipped detector. The data flow at the maximum luminosity is expected to be 12PB of data/year and will be analysed by a cutting-edge computing infrastructure spread over 26 Countries. Several of the major computing centres for HEP in Europe, USA and Canada will store the second copy of RAW data. In this scenario, the international network infrastructure for research plays a key role in supporting and orchestrating all the activities of data analysis and replication. The large-scale network data challenge will also take advantage from LHCONE VRF service and the support of network experts of KEKCC, Belle II sites and NREN. The program of major upgrade in 2019 empowered the connection among Japan, Europe and USA over a 100Gb geographic ring. In this work, we summarize the network requirements needed to accomplish all the tasks provided by the Belle II computing model. We also highlight the status of the major network links that support and advance Belle II. Lastly, we present the results of the last Network Data Challenge campaign performed between KEK and the main RAW data centres with the additional usage of the Data Transfer Node service provided by GÉANT.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.180 | 0.066 |
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".