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Record W4394824296 · doi:10.1016/j.dib.2024.110420

A global electricity transmission database for energy system modelling

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

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

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.568

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 science0.0000.000
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 head0.020
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