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Record W2394164044

Advanced Metering Infrastructure

2009· article· en· W2394164044 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

VenueNanfang dianwang jishu · 2009
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsSmart gridSmart meterAutomatic meter readingMetering modeTelecommunicationsAutomationVisibilityTelecommunications networkAsset managementEngineeringProcess (computing)GridElectric power systemEnergy management systemSystems engineeringComputer scienceEnergy managementElectrical engineeringPower (physics)Energy (signal processing)WirelessBusiness
DOInot available

Abstract

fetched live from OpenAlex

Advanced Metering Infrastructure(AMI) is the totality of systems and networks for measuring, collecting, storing, analyzing, and using energy usage data.This paper provides an overview of the four parts of AMI technology(i.e.smart meter, wide area communication network;meter data management system, MDMS;and home area networks, HAN), the AMI effect, and its benefits for smart-grid development.Through system-wide communication networks AMI will link consumers and power utilities together and provide foundation for future distribution automation and other smart-grid functionalities.The system-wide measurement and visibility enabled by AMI will enhance the utilities' system operation and asset management process.It is recommended that the utilities should take advantage of AMI technology development and implementation to plan and build a common-integrated communication network and IT system in order to realize business transformation and to shape the power system towards a smart-grid.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.554

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)0.0000.000

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 head0.002
GPT teacher head0.188
Teacher spread0.186 · 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