Special Issue: The Best of CCGrid'2007: A Snapshot of an ‘Adolescent’ Area
Bibliographic record
Abstract
It is with great pleasure that we introduce you to this special issue with the best work presented in CCGrid'2007, the 7th IEEE International Symposium on Cluster Computing and the Grid, held in Rio de Janeiro, Brazil, on May 14-17, 2007.Over the years, CCGrid has become a premium conference of truly international coverage, bringing together researchers and practitioners, and enabling them to share their insight, results, and experience in the multi-faceted areas of Grid and cluster computing.Overall, 330 people attended the event, a CCGrid all-time record.This is a testimony to the fact that the grid and cluster computing communities continue to grow, and many early projects that were started in the beginning of the decade (when the conference series began) are now maturing and producing significant results.The international coverage and impact of CCGrid are also worth highlighting.After Australia, Germany, Japan, the United States of America, the United Kingdom, and Singapore, CCGrid comes to Brazil without yet repeating a host country.Similarly, these proceedings carry papers from 22 different countries: Algeria,
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.105 | 0.068 |
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".