Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is our great pleasure to welcome you all to the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDE'10), held in conjunction with SIGMOD 2010. 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'10 is the ninth 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), co-located with MobiCom 2003. The 4th, 5th, 6th, 7th, and 8th MobiDE workshops took place in Baltimore, USA (June 2005), Chicago, USA (June 2006), Beijing, China (June 2007), Vancouver, Canada (June 2008), and Providence, USA (June 2009), respectively, co-located with SIGMOD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it