Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Bibliographic record
Abstract
It's a great pleasure to welcome you to the 12th joint ACM SIGMETRICS and IFIP PERFORMANCE International Conference, hosted by the Department of Computing, Imperial College London - one week after the Queen's Silver Jubilee celebrations and six weeks before the 2012 Olympic Games, just the other side of Town. In fact we chose these dates so as to avoid clashing with Her Majesty's special week, which might have been compromised by an event of such stature! This year's conference enhances the tradition of both of its constituents' being the premier fora for state-ofthe-art research in performance modeling and measurement techniques, tools and applications in the American and Europe continents, respectively. We have assembled a superb technical program with 31 full papers of the highest quality and 23 posters highlighting innovative research; further details are provided in the Program Chairs' Welcome that follows. Contributors come from 18 different countries in 3 continents; 49 papers have (co-)authors from academic institutions and 22 have industrial (co-)authors. This year we have increased the numbers of Tutorials and Workshops. On Monday, 11th June there are five tutorials and two workshops: the ever-popular GreenMetrics and, for the first time, W-PIN. My thanks to Cati Llado for her organizing the tutorials and expanding this aspect of the conference. On Friday we have three more innovative Workshops: the long-established MAMA, a new one PADE and a Hands-on Tutorial-Workshop NetFPGA. We hope as many of you as possible will take advantage of these excellent satellite events.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.027 |
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".