Proceedings of the 4th ACM international workshop on Experimental evaluation and characterization
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 4th ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization (WiNTECH) 2009 and to the city of Beijing! Following the three previous successful editions in San Francisco, Montreal and Los Angeles, WiNTECH has now established itself as a high quality forum that brings together researchers sharing new ideas and experiences in experimental wireless systems and networks. This year, WiNTECH received 25 paper submissions. After a thorough review by the members of the Technical Program Committee, 9 papers were selected for publication. We believe that thanks to the high-quality submissions, we can present a strong and broad technical program. The topics span a significant part of experimental wireless networks' research including methodologies, tools and techniques for both mobile and static wireless network testbeds. We trust you will find the papers both stimulating and valuable. In addition to paper presentations, we will continue with the innovations introduced at last year's WiNTECH program. The workshop will be one full day, featuring 9 papers, 5 posters, and 9 demos which will compete in the WinCool demo contest.
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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 | 0.062 | 0.086 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".