Simulation and user analysis of BaBar data in a distributed cloud
Bibliographic record
Abstract
We present a distributed cloud computing system that is being used for the simulation and analysis of data from the BaBar experiment. The clouds include academic and commercial computing sites across Canada and the United States that are utilized in a unified infrastructure. Users retrieve a virtual machine (VM) with pre-installed application code; they modify the VM for their analysis and store it in a repository. The users prepare their job scripts as they would in a standard batch environment and submit them to a Condor job scheduler. The job scripts contain a link to the VM required for the job. A separate component, called Cloud Scheduler, reads the job queue and boots the requiredVMon one of the available compute clouds. The system is able to utilize clouds configured with various cloud Infrastructure-as-a-Service software such as Nimbus, Eucalyptus and Amazon EC2. We find that the analysis jobs are able to run with high efficiency even if the data is located at distant locations. We will show that the distributed cloud system is an effective environment for user analysis and Monte Carlo simulation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".