MétaCan
Menu
Back to cohort
Record W4394824296 · doi:10.1016/j.dib.2024.110420

A global electricity transmission database for energy system modelling

2024· article· en· W4394824296 on OpenAlexaff
Maarten Brinkerink, Gordon F. Sherman, Simone Osei-Owusu, Reema Mohanty, Aman Shah Abdul Majid, Trevor Barnes, Taco Niet, Abhishek Shivakumar, Erin Mayfield

Bibliographic record

VenueData in Brief · 2024
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsSimon Fraser University
FundersForeign, Commonwealth and Development OfficeGovernment of the United Kingdom
KeywordsComputer scienceElectricity systemElectricityRepresentation (politics)Energy (signal processing)DatabaseElectric power transmissionEnergy modelingTransmission (telecommunications)Energy systemData scienceData miningTelecommunicationsElectricity generationPower (physics)Engineering

Abstract

fetched live from OpenAlex

Energy system modelling can be used to provide scenario-based insights in energy system transition pathways. However, data accessibility is a common barrier for the model representation of energy systems, both regarding existing infrastructure, as well as planned developments consistent with current policies. This paper describes the 'Global Transmission Database', the first global dataset covering existing and planned electricity transmission developments between countries and selected regions. The dataset can be used as a starting point for the representation of cross-regional electricity grids globally in energy system models and other computational tools. All data is collected from publicly available sources and combined into a single machine-readable format for convenient application.

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)0.006
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.046
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0460.026

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 head0.020
GPT teacher head0.238
Teacher spread0.218 · 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
GenreDataset

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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueData in BriefSame topicIntegrated Energy Systems OptimizationFrench-language works237,207