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Record W7006717496

WHO PAYS FOR RENEWABLES? THE EFFECT OF\nDATACENTRES ON RENEWABLE SUBSIDIES. ESRI Research Bulletin 2019/11

2019· other· en· W7006717496 on OpenAlexaff

Bibliographic record

VenueArchive of European Integration (AEI) (University of Pittsburgh) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsCanadian Bulletin of Medical History
Fundersnot available
KeywordsRenewable energyElectricityObligationElectricity systemRenewable resourceService (business)Electricity retailingElectricity demand
DOInot available

Abstract

fetched live from OpenAlex

Ireland faces several targets for renewable energy usage, across the heating,\ntransport and electricity sectors. These targets are set as a proportion of total\nenergy usage. In the case of electricity, 40% of electricity must be generated from\nrenewable sources by 2020. To meet this target, renewable electricity generation\nis subsidised through the Public Service Obligation levy, which appears on all\nconsumers’ bills. The PSO is levied on residential consumers, commercial\nconsumers and large industrial consumers according to their contribution to peak\ndemand – the more the sector contributes to peak demand, the higher the\nportion of PSO that they pay.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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

Explore more

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