MétaCan
Menu
Back to cohort
Record W4399330307 · doi:10.1109/mele.2024.3385949

Enhancing Distributed Energy Resource Integration and Supply Reliability: The Two-to-One rule

2024· article· en· W4399330307 on OpenAlexaff
Mukesh Nagpal, Kenan Hadzimahovic, Amit Bimbhra

Bibliographic record

VenueIEEE Electrification Magazine · 2024
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsReliability (semiconductor)Reliability engineeringResource (disambiguation)Computer scienceDistributed computingMathematical optimizationEngineeringMathematicsComputer networkPhysics

Abstract

fetched live from OpenAlex

Society’s increasing dependence on small renewable resources, generating less than 10 MVA, for sustainable electricity brings forth a unique challenge. Unlike large and centralized generation facilities, these resources often remain unconnected to the transmission system because there are prohibitive costs associated with high-voltage interconnection equipment. Consequently, they find their connection in medium- or low-voltage distribution systems, originally designed for customer supply rather than for securing the integration of generation resources. This shift transforms the distribution feeder from a single source to a double-sourced (or multisourced) line, necessitating protection requirements like the transmission system. However, traditional transmission protection solutions prove too costly to integrate these small-generation resources into the distribution system.

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)0.008
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0070.015
Open science0.0030.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0070.004

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 head0.007
GPT teacher head0.209
Teacher spread0.203 · 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 designTheoretical or conceptual
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Electrification MagazineSame topicSmart Grid Energy ManagementFrench-language works237,207