IEEE Access Special Section Editorial: Future Networks: Architectures, Protocols, and Applications
Bibliographic record
Abstract
Wireless communications and networking have been continually evolving to improve and be a part of our lifestyle. This uninterrupted development is due to many research projects and practices that are being carried out to improve the quality of services and applications supported by networking technologies. Initially, the plethora of computer networks research was designed to allow users to share the thoughts and facts using textual data through addressing devices. Meanwhile, we have witnessed that users play the role of both producers and consumers at the same time. These new emerging requirements gave birth to Cloud Computing (CC), Data Centric Networking (DCN), and other advancements in IEEE standards. Moreover, researchers also intended to redesign the networking architectures and protocols with the focus on content rather than the host. The resulting new architectures are Information Centric Networks (ICN) with various extensions like Content Centric Networks (CCN), Named Data Networks (NDN), Data-Oriented Network Architecture, etc.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.019 | 0.017 |
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".