Proceedings of the Eighth ACM International Workshop on Data Engineering for Wireless and Mobile Access
Bibliographic record
Abstract
It is our great pleasure to welcome you all to the ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDE'09), held in conjunction with SIGMOD 2009. MobiDE continues its tradition of bringing together researchers and practitioners in databases, mobile computing, and networking, and providing a full day of exciting presentations and discussions. As in previous years, the workshop serves as a forum to present latest research and engineering results and contributions, and set future directions in wireless and mobile data management. MobiDE'09 is the eighth of a successful series of workshops that aims to act as a bridge between the data management, wireless networking, and mobile computing communities. The 1st MobiDE workshop took place in Seattle, USA (August 1999), in conjunction with MobiCom 1999. The 2nd MobiDE workshop was held in Santa Barbara, USA (May 2001), together with SIGMOD 2001. The 3rd MobiDE workshop was organized in San Diego, USA (September 2003), colocated with MobiCom 2003. The 4th, 5th, 6th and 7th MobiDE workshops took place in Baltimore, USA (June 2005), Chicago, USA (June 2006), Beijing, China (June 2007), and Vancouver, Canada (June 2008), respectively. This year's event marks the 10-year anniversary of the workshop. The final program covers a range of topics such as querying and security in mobile systems and applications, location-based/context-aware data management, database issues for mobile computing and pervasive systems. In addition, the workshop program includes two keynote speeches, the first one by Matt Welsh of Harvard University, USA, with title A New Era of Resource Responsibility for Sensor Networks and the second one by Frank Olken of the National Science Foundation, USA, with title Space, Time, Sensors, and Data Semantics. Finally, the workshop features a panel with title 20 Years of Mobile Data Management Research: Vision and Reality which is moderated by Panos K. Chrysanthis, of the University of Pittsburgh, USA. These proceedings will serve as a valuable reference point for the latest results on mobile and wireless data engineering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".