Grids Today, Clouds on the Horizon
Bibliographic record
Abstract
By the time of CCP 2008, the worldâs largest scientific machine â the Large Hadron Collider â should have been cooled down to its operational temperature of below 20K and injection tests should have started. Collisions of proton beams at 5 + 5 TeV are expected within one to two months of the initial tests, with data taking at design energy (7 + 7 TeV) now foreseen for 2009. In order to process the data from this world machine, we have put our âﾜHiggs in one basketâ â that of Grid computing. After many years of preparation, 2008 has seen a final âﾜCommon Computing Readiness Challengeâ (CCRCâ08) â aimed at demonstrating full readiness for 2008 data taking, processing and analysis. By definition, this relies on a worldâwide production Grid infrastructure. But change â as always â is on the horizon. The current funding model for Grids â which in Europe has been through 3 generations of EGEE projects, together with related projects in other parts of the world, including South America â is evolving towards a longâterm, sustainable eâinfrastructure, like the European Grid Initiative (EGI). At the same time, (potentially?) new paradigms, such as that of âﾜCloud Computingâ are emerging. This talk summarizes the (successful) results of CCRCâ08 and discusses the potential impact of future Grid funding on both regional and international application communities. It contrasts Grid and Cloud computing mode ls from both technical and sociological points of view. Finally, it discusses the requirements from production application communities, in terms of stability and continuity in the medium to long term.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".