Bibliographic record
Abstract
Future Network World Forum Welcome MessageWhile 5G deployment is in full swing in various parts of the world, there is already an onset of research, standardization and testbeds towards the next generation of cellular networks, 6G.Since its inception in 2018, IEEE Future Networks' flagship conference, IEEE 5G World Forum, has contributed to the evolution and adoption of 5G by way of technical papers, tutorials, standardizations, topicals, verticals, and demo sessions.We have brought the world's researchers, industry and academics together on the same platform.Due to COVID-19, 2020 and 2021 5G World Forums were organized in a virtual manner, instead of in Bangalore and Montreal, as planned.However, thanks to the initiative from the Montreal Section, the 2022 5th IEEE Future Networks World Forum took place in Montreal in a hybrid manner.Also, since the technology advancement is moving rapidly towards 6G, the organizing committee decided to change the name 5G World Forum to Future Networks World Forum, keeping it in sync with the IEEE Future Networks Technical Community.This will pave the way for development for creating a platform that will allow the researchers, academia, students, and industry to collaborate towards 5G and beyond, including 6G and whatever may follow.Our change to Future Networks World Forum also reflects the fact that we are not solely focused on wireless technology.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.421 | 0.319 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".