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Record W4401794765 · doi:10.1109/jiot.2024.3432968

Guest Editorial Special Issue on Next-Generation Multiple Access for Internet of Things

2024· editorial· en· W4401794765 on OpenAlexaff
Tianwei Hou, Xidong Mu, Zhiguo Ding, Octavia A. Dobre, Naofal Al‐Dhahir

Bibliographic record

VenueIEEE Internet of Things Journal · 2024
Typeeditorial
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceThe InternetComputer networkNext-generation networkTelecommunicationsWorld Wide Web

Abstract

fetched live from OpenAlex

The rapid development of next-generation Internet of Things (IoT) applications, including integrated-sensing-and-communication (ISAC), smart grids, smart cities, intelligent transport networks, etc., enables at least tens of billions of bandwidth-thirsty IoT devices, which consume a deluge of data in the sixth-generation (6G) communication systems. In addition, future challenging heterogeneous services and applications, such as Industry 4.0, require the provisioning of unprecedented massive device access, heterogeneous data traffic, high spectral efficiency, and low latency. As a result, there is an urgent demand to pay more attention to IoT networks for high-reliable and low-delay massive access.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.027
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.001
Science and technology studies0.0030.002
Scholarly communication0.0090.005
Open science0.0030.002
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0270.025

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.

Opus teacher head0.039
GPT teacher head0.307
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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