MétaCan
Menu
Back to cohort
Record W2611766964 · doi:10.15173/esr.v19i1.535

MEASURING LONG-TERM ENERGY SUPPLY RISKS: A G7 RANKING

2012· article· en· W2611766964 on OpenAlex
Manuel Frondel, Nolan Ritter, Christoph Μ. Schmidt

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnergy Studies Review · 2012
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsEnergy supplyEnergy securityRanking (information retrieval)Nuclear powerEconomicsEnergy (signal processing)Term (time)Resource (disambiguation)Supply and demandNatural resource economicsBusinessRenewable energyMacroeconomicsComputer science

Abstract

fetched live from OpenAlex

The security of energy supply has again become a similarly hot topic as it was during the oil crises in the 1970s, not least due to the recent historical oil price peaks. In this paper, we analyze the energy security situation of the G7 countries using a statistical risk indicator and empirical energy data for the years 1978 through 2010. We find that Germany's energy supply risk has risen substantially since the oil price crises of the 1970s, whereas France has managed to reduce its risk dramatically, most notably through the deployment of nuclear power plants. As a result of the nuclear phase-out decision of 2011, Germany's supply risk can be expected to rise further and to approach the level of Italy. Due to its resource poverty, Italy has by far the highest energy supply risk among G7 countries.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.134
GPT teacher head0.345
Teacher spread0.211 · 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