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Record W4379794035 · doi:10.1109/miot.2023.10145023

Call for Papers

2023· paratext· en· W4379794035 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Internet of Things Magazine · 2023
Typeparatext
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsYork University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.283
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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