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Record W7132433359

Smart Grids Innovation Challenge Country Report 2019

2019· other· en· W7132433359 on OpenAlexaboutno aff
Helfried Brunner1 Michael Hübner 1, Anjali Wadhera2 , Lu Zongxiang3, Zhao Bin3, Zhang Jia3, Wang Huan3, Yang Zilong3, Jacob Østergaard4, Henrik W. Bindner4 , Kari Mäki5, Matti Aro5, Pia Salokoski5 , BARRETEAU Julien6, Ralf Eickhoff7, Karl Waninger7, Michele de Nigris8, Luca Orrù8, Giorgio Graditi8, Marialaura Di Somma8, Francesco Sergi8, Ricardo Perez Sanchez8 , Kjell Sand9, Birgit Hernes9, Grete Håkonsen Coldevin9, Erland Eggen9, Joonhyung Ahn10, Taehong Sung10,| Jun-Tae Kim10 , Abdullah A. Almehizia 11, Fredrik Lundström12, Alex

Bibliographic record

VenueCNR ExploRA · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSmart gridRenewable energyEuropean commissionField (mathematics)CommissionPrivate sectorEnergy sectorInformation sharing
DOInot available

Abstract

fetched live from OpenAlex

The Smart Grids Country Report 2019 is an IC1 outstanding collaborative achievement gathering contributions from 16 countries (Australia, Austria, Canada, China, Denmark, Finland, France, Germany, India, Italy, Norway, Republic of Korea, Saudi Arabia, Sweden, United Kingdom, United States of America) and the European Commission representing the European Union. The Country Report 2019 collects information updated till 2018 about energy strategies, trends, projects and actions underway in the field of smart grids in the participating countries. Each contributor provided information about status and framework of smart grids implementation, renewable energy deployment, National and International programmes related to smart grids and renewable energy and relevant case studies, sharing experience in R&D activities to facilitate governmental investments and to enable private sector and investors engagement.

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), 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: Other · Consensus signal: Other
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.148

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 head0.041
GPT teacher head0.281
Teacher spread0.241 · 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
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

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