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Record W4412656305 · doi:10.1038/s44264-025-00079-9

Renewable energy sources for arctic food sufficiency and sustainability

2025· article· en· W4412656305 on OpenAlexaff
Getu Hailu, Majdi Abou Najm, Paul Eric Aspholm, Tirupati Bolisetti, Colleen Charles, Ranjan Datta, Trine Eggen, Belinda Flem, Margot Hurlbert, Meriam G. Karlsson, Arthur Nash, Narasinha Shurpali, Radha Sivarajan Sajeevan, David Parsons, Adrian Unc, Govert Valkenburg, Danielle Wilde, Bing Wu, Sandra F. Yanni, Debasmita Misra

Bibliographic record

Venuenpj Sustainable Agriculture · 2025
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsAgriculture and Agri-Food CanadaMemorial University of NewfoundlandMount Royal UniversityMcGill UniversityFirst Nations University of CanadaUniversity of ReginaUniversity of Windsor
Fundersnot available
KeywordsSustainabilityRenewable energyArcticNatural resource economicsEnvironmental scienceThe arcticEnvironmental economicsBusinessEnvironmental resource managementEconomicsOceanographyEngineeringEcologyGeology

Abstract

fetched live from OpenAlex

One of the UN’s 17 sustainable development goals (SDGs), SDG 7, is to “ensure access to affordable, reliable, sustainable and modern energy for all.” This goal addresses the need for environmental sustainability while highlighting energy’s vital role in promoting social and economic justice. It calls for sustainable, affordable, modern, and reliable energy usage for the health and well-being of society while mitigating climate change. Here, we briefly review available literature and data to examine how renewable energy, food security, and sustainability are interconnected in Arctic countries and regions, and how these regions can “ensure access to affordable, reliable, sustainable and modern energy for all” and progress towards achieving food self-sufficiency by integrating renewable energy sources into food production systems. We analyze several case studies to draw conclusions on how Arctic communities can become resilient, sustainable, and economically prosperous by promoting local food production while preserving cultural practices.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.233
Teacher spread0.228 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations1
Published2025
Admission routes1
Has abstractyes

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