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Record W3158760877 · doi:10.1109/mcom.2021.9422329

Series Editorial: Internet of Things and Sensor Networks

2021· article· en· W3158760877 on OpenAlexaff
Sergey Andreev, Ngọc Dũng Đào, Prasant Misra

Bibliographic record

VenueIEEE Communications Magazine · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsComputer scienceInternet of ThingsEnhanced Data Rates for GSM EvolutionThe InternetPandemicEdge computingWork (physics)TelecommunicationsComputer securityInternet privacyTelemedicineCoronavirus disease 2019 (COVID-19)Data scienceFace (sociological concept)Health careWorld Wide Web

Abstract

fetched live from OpenAlex

Today, the Internet of Things (IoT) continues to evolve as a predominant technical trend. In the face of the global pandemic, many conventional IoT paradigms are, however, expected to shift in response to pressing societal challenges. For instance, we are preparing to observe substantial investments in the telemedicine and healthcare sectors as well as efficient work-from-home solutions. This accentuates the need for edge intelligence in supporting the increasingly massive IoT deployments augmented with machine learning capabilities, among many others.

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)0.006
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.029
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0060.003
Science and technology studies0.0040.003
Scholarly communication0.0120.007
Open science0.0040.002
Research integrity0.0120.018
Insufficient payload (model declined to judge)0.0290.029

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 head0.051
GPT teacher head0.285
Teacher spread0.234 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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