s Experience with the Preservation of Data and Analysis Capabilities
Bibliographic record
Abstract
\nThe BABAR experiment collected electron-positron collisions at the SLAC National Accelerator Laboratory (SLAC) from 1999-2008. Although data taking has stopped 15 years ago, the collaboration is still actively doing data analyses, publishing results, and giving presentations at international conferences. Special considerations were needed to do analyses using a computing environment that was developed decades ago. A framework is required that preserves the data, data access, and the capability of doing analyses using a well defined and preserved environment. Also, BABAR’s support by SLAC ended at the beginning of 2021. Fortunately, the High Energy Physics Research Computing group at the University of Victoria (UVic), Canada, offered to provide the new home for the main BABAR computing infrastructure, the Grid Computing Centre Karlsruhe offered to host all data for access by analyses running at UVic, and CERN and the IN2P3 Computing Centre offered to store a backup of all data. This paper presents what was done at BABAR to preserve the data and analysis capabilities and what was needed to move the whole computing infrastructure, including collaboration tools and data files, away from SLAC. It will be shown how BABAR preserved the ability to continue to do data analyses and also have a working collaboration tools infrastructure. This paper will describe on BABAR’s experience with such a big change in its infrastructure and what was learned from it, which may be useful to other experiments which are interested in long term analysis support and data preservation in general.\n
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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 | 0.051 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".