Guest Editorial Deployment Issues and Performance Challenges for 5G, Part I
Bibliographic record
Abstract
There are rapid developments towards the deployment of 5G and hardly a day goes by without the release of a major announcement concerning 5G. Many of these developments are happening in parallel as there is a race against time for 5G deployment. Various test beds have been established, laboratory trials and proof of concept trials of individual building blocks of 5G are already underway or have been completed, large scale system trials are also happening or are planned and standardisation efforts in the ITU, 3GPP, IEEE, etc. are also in progress. In the case of ITU/3GPP, the standards are expected to be completed by 2019. Many candidate bands for 5G in the centimetric and mm wave range were identified by the World Radio Conference (WRC) 2015 and are expected to be finalized by WRC 2019, while coexistence studies of 5G and existing systems in the new candidate bands are underway.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".