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
Once considered only a limited mean for short-range communication of best-effort traffic, Wi-Fi's roots have speared into way farther lands. Voice-over-Wi-Fi (VoWiFi) is an alternative data-driven IP-based dialing service for indoor users with poor cellular coverage. The recent rollout of iPhone6+ running IOS8 with novel Wi-Fi dialing feature spurred several operators around the world to invest on VoWiFi as a complementary service. As such, when cellular coverage is poor, calls are placed/switched over to wireless local area network (WLAN) in a transparent manner to users. Given the heterogeneity of the setup, a high uncertainity arises regarding whether the switching (handoff) task remains unnoticed to the users or not. There is also the issue of interference over WLAN as well as congestion in internet core that is often added to the obscurities. Inspired by these facts, this paper investigates the suitability of VoWiFi in satisfying the voice service quality requirements for home and small network users. Three different architectures are investigated based on handoff, load-balancing and repeater. The voice reception quality in terms of jitter, delay and packet loss is monitored for each architecture. It is observed that delay, jitter and packet loss are improved in repeater-based scenario whereas call drop is experienced in both handoff and load balancing scenarios.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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