Bibliographic record
Abstract
Google recently introduced U.S. pilot Project Fi in cooperation with two major 4G LTE network operators. Project Fi intelligently connects mobile users to free open WiFi hotspots or otherwise, if not possible, seamlessly moves them between the two partner LTE networks for delivering the most reliable and fastest available wireless service. Project Fi may be an important step towards meeting the 5G requirements of ultra-high reliability and very low latency while taking economic considerations into account. Following the integrative vision of 5G, this paper attempts to provide further insights into the potential of Project Fi wireless service by elaborating on the integration of 4G LTE-Advanced (LTE-A) heterogeneous networks (HetNets) and low-cost data-centric Ethernet based fiber-wireless (FiWi) broadband access networks with not only WiFi offloading but also cost-saving fiber infrastructure sharing capabilities for small cell backhaul, which is becoming a major performance-limiting factor in mobile networks. Furthermore, given that decentralization is another important aspect of the 5G vision, we develop a decentralized routing algorithm for the resultant FiWi enhanced LTE-A HetNets.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".