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Record W4207000003 · doi:10.1109/mcom.2021.9475153

Series Editorial: Network Softwarization and Management

2021· article· en· W4207000003 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 Communications Magazine · 2021
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsComputer scienceSeries (stratigraphy)Data scienceTelecommunications

Abstract

fetched live from OpenAlex

This series focuses on softwarization, management, and their integration in communication networks and services. “Network Softwarization” advocates for network architectures that separate the software implementing network functions, protocols and services from the hardware running them. “Network Management” aims to integrate fault, configuration, accounting, performance, and security capabilities in the network and to support self-management features, integral automation, and autonomic capabilities, empowering the network with inbuilt cognition and intelligence. The critical role that software and Management are increasingly playing in telecommunications is enabling unprecedented levels of abstraction, disaggregation, operation, integration, robustness, optimization, intelligence, precision delivery, programmability and cost and complexity reduction of infrastructures and services. Such an approach is resulting in even greater attainment of non-functional characteristics (e.g., qualities of the operation of a network, rather than specific behaviors, integrability, interoperability, operational guarantees, deployability, auditability and control, reliability, adaptability, elasticity, effectiveness, extensibility, automation and autonomicity).

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: Methods · Consensus signal: Methods
Teacher disagreement score0.343
Threshold uncertainty score0.651

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
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 head0.018
GPT teacher head0.249
Teacher spread0.231 · 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