MétaCan
Menu
Back to cohort
Record W2394164044

Advanced Metering Infrastructure

2009· article· en· W2394164044 on OpenAlexaff
Wenpeng Luan

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.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.045
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.022

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

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

Citations160
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueNanfang dianwang jishuSame topicSmart Grid Security and ResilienceFrench-language works237,207