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Record W4393758839 · doi:10.5281/zenodo.10063445

Global Transmission Database

2025· dataset· en· W4393758839 on OpenAlexaffabout
Maarten Brinkerink, Gordon F. Sherman, Simone Osei-Owusu, Reema Mohanty, Aman Shah Abdul Majid, Trevor Barnes, Taco Niet, Abhishek Shivakumar, Erin Mayfield

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDatabaseComputer science

Abstract

fetched live from OpenAlex

The Global Transmission Database (GTD) consists of comprehensive data regarding existing and planned cross-border transmission capacities globally collated from public sources. The dataset is oriented towards representing entry level capacity data (MW) that can be used in energy system models and other computational tools. Transmission capacities are provided at a country-to-country basis in addition to regional level data for a sample of larger countries (Australia, Brazil, Canada, China, India, Indonesia, Japan, Philippines, Russian Federation, United States, Vietnam). Capacities are provided for land-based transmission pathways as well as for subsea pathways. Refer to the accompanying paper for details on the applied methodology as well as a file by file description of the repository content.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0230.003

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 head0.252
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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