of the requirements of the Physics Co-op Program
Bibliographic record
Abstract
Modern particle physics experiments, such as the ATLAS experiment invloving the Large Hadron Collider (LHC) under development in CERN, are exceeding the limits of computational resources available at any single location. The solution is to form computational grids, networking computing clusters worldwide to provide an unprecedented amount of processor power and storage space required for these new scientific endeavors. Many grids are being formed all over the world. Most noteably, the Large Hadron Collider Computing Grid (LCG) is linking together computing clusters worldwide in an effort to prepare for the onslaught of data expected when the ATLAS project goes online on 2007. Canadian researchers are involved, and GridX1, an experimental grid consisting of clusters at the Uiversity of Victoria, University of Alberta, and the National Research Council, has recently joined forces with the LCG. As a result, ATLAS simulation jobs are being run on GridX1 (as well as other worldwide LCG sites) to encourage testing and development of grid technology. One of these developments has been a basic monitoring framework for GridX1, enabling grid users to access information regarding current cluster status and availability, as well as detailed job status information. This monitoring framework has been developed using existing grid software as well as other open source Unix packages, which are interfaced with Perl scripts, web development tools, and databases to provide a basic working framework for an expandable grid monitoring system. The tools used to develop the GridX1 monitoring framework will be discussed, as will the workings of the individual existing components in the framework
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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.331 | 0.382 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".