The 2014 ACM international conference on Measurement and modeling of computer systems
Bibliographic record
Abstract
It is our pleasure to welcome you to SIGMETRICS 2014. SIGMETRICS is the flagship conference of the ACM special interest group for the computer systems performance evaluation community. This year's conference continues the long-standing SIGMETRICS tradition to publish the highestquality research on the development and application of state-of-the-art, broadly applicable analytic, simulation, and measurement-based performance evaluation techniques. We are pleased to present a diverse set of papers in areas such as sensor, mobile and wireless networks, queuing and scheduling, msocial networks, memory technologies, large-scale measurement studies, system tracing and monitoring, data center resource provisioning and energy management. Our authors hail from 13 countries on 4 continents and represent both academia and industry. SIGMETRICS 2014 received 237 submissions, the second highest number since the founding of this SIG. Of these, we accepted 40 papers, the largest in the history of the conference, while still maintaining a highly competitive acceptance rate of 16.8%. During the review process, the Program Committee provided 4-6 reviews for each paper and made extensive use of HotCRP's Comment feature for online discussions. The Program Committee then met in person in a 1.5-day meeting on February 7-8, 2014, in Toronto, Canada, and selected 40 papers to be included as full papers in the technical program. In addition, 31 papers were invited as 2-page posters, and the authors of 30 of these papers accepted our invitation. As an experiment, we invited for the first time also all authors of full papers to present a poster version of their paper during one of the breaks at the conference to foster interaction between authors and attendees. We used Eddie Kohler's excellent HotCRP software to manage all stages of the review process, from submission to author notification.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.033 |
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".