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Record W3074319202 · doi:10.1109/jrfid.2020.3016166

IEEE Council on Radio-Frequency Identification: History, Present, and Future Vision

2020· article· en· W3074319202 on OpenAlexaff
Fei‐Yue Wang, Gisele Bennett, Nazanin Bassiri‐Gharb, Yidong Li, Jun Jason Zhang, Gregory D. Durgin, Shahriar Mirabbasi, Pui Yi Lau, Christopher R. Valenta, Francesco Amato, Thanunathan Rangarajan, Mohammad Alhassoun

Bibliographic record

VenueIEEE Journal of Radio Frequency Identification · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Research in Systems and Signal Processing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsScope (computer science)Identification (biology)Transformational leadershipComputer scienceRadio-frequency identificationTelecommunicationsCyber-physical systemPath (computing)Data scienceComputer securityPolitical sciencePublic relationsComputer network

Abstract

fetched live from OpenAlex

This article summarizes the history and present state of the IEEE Council on Radio-Frequency Identification (CRFID). The aim, scope, and achievement of CRFID on technical & academic activities, publications, and membership services & education are highlighted, with focus on how CRFID commits and grows resources to achieve its goals for its technical communities. This article also provides the vision and path to the council's future, and on this path, CRFID is broadening the technological frontiers of IoT, Blockchain, Cyber-Physical Systems (CPS), Cyber-Physical Social Systems (CPSS), Digital Twins, Parallel Systems, Parallel Intelligence, and many other related enabling and transformational techniques.

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)0.016
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0040.004
Scholarly communication0.0070.006
Open science0.0020.005
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0090.005

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 head0.047
GPT teacher head0.267
Teacher spread0.220 · 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
GenreEmpirical

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

Citations14
Published2020
Admission routes1
Has abstractyes

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