Call for Papers
Bibliographic record
Abstract
The sixth generation (6G) mobile networks aim to provide integrated sensing, communication, localization, and computing services with low latency, high capacity, and high reliability in a real-time communication environment. The emergence of novel technologies is required to achieve a sustainable capacity growth at a low cost and a low energy consumption while meeting diverse Quality-of-Service (QoS) requirements. The Internet-of-Things (IoT) applications in smart cities, intelligent transportation, and entertainment focused services, like augmented and virtual reality or holographic telepresence, are quickly emerging and accelerating the development of new technologies to meet the strict requirements of future mobile networks. However, the wireless channel is an unavoidable factor and a primary hindrance in the performance. In addition to being uncontrolled, the wireless propagation channel has an unavoidable detrimental impact on the precision and adaptability of IoT networks. Such limitations motivate a need of reconfigurable intelligent surfaces (RISs) to facilitate IoT application with low energy consumption, low cost, and low latency.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.849 | 0.787 |
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".