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Record W4306173614 · doi:10.1038/s41597-022-01737-0

OSeMOSYS Global, an open-source, open data global electricity system model generator

2022· article· en· W4306173614 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScientific Data · 2022
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity College CorkLoughborough UniversityImperial College LondonScience Foundation IrelandGovernment of CanadaGovernment of the United KingdomForeign, Commonwealth and Development OfficeColorado School of Mines
KeywordsOpen sourceOpen dataElectricityOpen system (computing)Computer scienceGenerator (circuit theory)Electricity systemWorld Wide WebElectricity generationOperating systemEngineeringElectrical engineeringSoftwarePhysicsPower (physics)

Abstract

fetched live from OpenAlex

This paper describes OSeMOSYS Global, an open-source, open-data model generator for creating global electricity system models for an active global modelling community. This version of the model generator is freely available and can be used to create interconnected electricity system models for both the entire globe and for any geographically diverse subset of the globe. Compared to other existing global models, OSeMOSYS Global allows for full user flexibility in determining the time slice structure and geographic scope of the model and datasets, and is built using the widely used fully open-source OSeMOSYS energy system model. This paper describes the data sources, structure and use of OSeMOSYS Global, and provides illustrative workflow results.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0040.004
Open science0.0240.021
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 head0.067
GPT teacher head0.295
Teacher spread0.227 · 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