A Step Towards a Computing Grid for the LHC Experiments: ATLAS Data Challenge 1
Bibliographic record
Abstract
The ATLAS Collaboration at CERN is preparing for the data taking and analysis at the LHC that will start in 2007. Therefore, a series of Data Challenges was started in 2002 whose goals are the validation of the Computing Model, of the complete software suite, of the data model, and to ensure the correctness of the technical choices to be made for the final offline computing environment. A major feature of the first Data Challenge (DC1) was the preparation and the deployment of the software required for the production of large event samples as a worldwide distributed activity. It should be noted that it was not an option to "run the complete production at CERN" even if we had wanted to; the resources were not available at CERN to carry out the production on a reasonable time-scale. The great challenge of organising and carrying out this large-scale production at a significant number of sites around the world had therefore to be faced. However, the benefits of this are manifold: apart from realising the required computing resources, this exercise created worldwide momentum for ATLAS computing as a whole. This report describes in detail the main steps carried out in DC1 and what has been learned form them as a step towards a computing Grid for the LHC experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".