Generic vertical handoff decision function for heterogeneous wireless
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
As mobile wireless networks increase in popularity and pervasiveness, we are facing the challenge of integration of diverse wireless networks such as WLANs and WWANs. Consequently, it is becoming progressively more important to arrive at a vertical handoff solution where users can move among various types of networks efficiently and seamlessly. The ability to remain connected as a mobile device roams across different types of networks still remains an unachieved objective. Frequently, just choosing the best network to connect to, is a challenging problem due to the large number of network characteristics that need to be considered. Identifying these decision factors is therefore one of the principal objectives for seamless mobility. In this paper, we discuss the different factors and metric qualities that give an indication of whether or not a handoff is needed. We then describe a vertical handoff decision function, VHDF, which enables devices to assign weights to different network factors such as monetary cost, quality of service, power requirements, personal preference, etc.
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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 | 0.000 | 0.000 |
| 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