Proceedings of the 3-rd IEEE International Workshop on Mobility Management and Wireless Access (MobiWac 2005) of the IEEE International Conference on Wireless Networks, Communications and Mobile Computing (WirelessCom 2005)
Bibliographic record
Abstract
It is our pleasure to welcome you to the 3-rd IEEE International Workshop on Mobility Management and Wireless Access (MobiWac 2005). This year MobiWac is being held in Maui, Hawaii, jointly with the World’s International Conference on Wireless Networks, Communications and Mobile Computing (WIRELESSCOM 2005). \n \nWe are pleased with the excellent technical program that was selected this year for MobiWac. We received more than 30 paper submissions from all over the world. After a careful review process, 12 papers were accepted for regular presentation. Moreover, 3 papers were accepted for short presentations. The accepted papers are from 9 countries (US, Canada, Taiwan, Thailand, Korea, Italy, France, Belgium, Iran.) - a fact that reflects the true International nature of the Workshop. \n \nMobiWac success indicates the importance of mobility management and wireless access issues in the design, evaluation and deployment of wireless and mobile systems. We really hope that the MobiWac proceedings will be a valuable reference for future research in the field.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.042 |
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".