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Record W4399304205 · doi:10.1109/mvt.2024.3390888

Special Issue on Integrated Sensing and Communications [From the Guest Editors]

2024· article· en· W4399304205 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 Vehicular Technology Magazine · 2024
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsHuawei Technologies (Canada)Memorial University of Newfoundland
Fundersnot available
KeywordsTelecommunicationsComputer scienceEngineeringSystems engineering

Abstract

fetched live from OpenAlex

The 6G networks are anticipated to be instrumental in powering a wide array of upcoming applications, including smart cities and homes, interconnected vehicles, intelligent factories, and the industrial Internet of Things (IoT). These applications need advanced wireless connectivity and robust, precise sensing abilities. A consistent aspect of future 6G plans is the increased importance of sensing, set to play an unprecedented role. By incorporating sensing capabilities, 6G networks will expand beyond traditional communication boundaries, offering pervasive sensing services to analyze and potentially map out the environments they operate in. This capability to collect environmental sensory data is seen as essential for nurturing intelligence in the upcoming era of smart environments. This necessitates a concurrent focus on developing communication and sensing technologies within 6G networks, which has prompted recent explorations in integrated sensing and communications (ISAC).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.237
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