Bibliographic record
Abstract
The Belle II experiment will record a similar quantity of data to LHC experiments and will acquire it at similar rates. This requires considerable computing, storage and network resources to handle not only data created by the experiment but also considerable amounts of simulated data. Consequently Belle II employs a distributed computing system to provide the resources coordinated by the the DIRAC interware. DIRAC is a general software framework that provides a unified interface among heterogeneous computing resources. In addition to the well proven DIRAC software stack, Belle II is developing its own extension called BelleDIRAC. BelleDIRAC provides a transparent user experience for the Belle II analysis framework (basf2) on various environments and gives access to file information managed by LFC and AMGA metadata catalog. By unifying DIRAC and BelleDIRAC functionalities, Belle II plans to operate an automated mass data processing framework named a "production system". The Belle II production system enables large-scale raw data transfer from experimental site to raw data centers, followed by massive data processing, and smart data delivery to each remote site. The production system is also utilized for simulated data production and data analysis. Although development of the production system is still on-going, recently Belle II has prepared prototype version and evaluated it with a large scale simulated data production. In this presentation we will report the evaluation of the prototype system and future development plans.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.203 | 0.166 |
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".