MétaCan
Menu
Back to cohort
Record W3201433106 · doi:10.3390/su131810308

The Risks of Global Heating to Energy Systems and Energy Security

2021· article· en· W3201433106 on OpenAlexaffabout
Larry Hughes, Moniek de Jong

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHydroelectricityElectricityExtreme weatherClimate changeWork (physics)Mains electricityEnergy securityGlobal warmingEnergy supplyNatural resource economicsElectricity generationBusinessEnvironmental scienceEnvironmental resource managementRenewable energyEnergy (signal processing)Power (physics)EngineeringEconomicsEcology

Abstract

fetched live from OpenAlex

As recent events have shown, global heating is increasing the risk to many sectors of society, from agriculture to the built environment, to transportation. The energy security of most energy systems, regardless of their size, is also at risk from the effects of global heating: The reliable supply of power to end users can be threatened by extreme weather events affecting transformers and transmission and distribution networks. It can also be a threat to generators that are vulnerable to unpredictable supplies of water, such as thermal or hydroelectric generation facilities. In this paper, we use an energy security methodology to examine some of the possible climate risks to the supply of power from hydroelectricity produced by one of western Canada’s electricity suppliers. The work is of particular interest because it shows how the increasing number of heatwaves that are affecting parts of western Canada and the Pacific Northwest could affect electricity supply in some western Canadian provinces and northern U.S. states.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.299
Teacher spread0.286 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueSustainabilitySame topicGlobal Energy Security and PolicyFrench-language works237,207