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Record W4318816122 · doi:10.5802/crgeos.193

Estimating theoretical stress regime for engineered geothermal energy systems in an arctic community (Kuujjuaq, Canada)

2023· article· en· W4318816122 on OpenAlexafffundabout
Mafalda M. Miranda, Jasmin Raymond, Chrystel Dezayes

Bibliographic record

VenueComptes Rendus Géoscience · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologies
KeywordsGeothermal gradientArcticGeothermal energyStress (linguistics)Environmental scienceThe arcticEnergy (signal processing)GeologyOceanographyStatisticsMathematicsGeophysics

Abstract

fetched live from OpenAlex

In remote northern regions, lacking deep geothermal exploratory boreholes, a theoretical approach to provide a first-order estimate of the stress regime seems a useful tool. Literature data was used in this context to evaluate the orientation of the stress components and empirical relationships were applied to calculate their magnitude in a community of Nunavik, northern Quebec. A Monte Carlo-based sensitivity analysis was carried out due to the uncertainty of the input parameters. Mohr–Coulomb friction and slip tendency analyses were additionally undertaken to assess the stress state and potential reactivation of existing fractures. The results highlight how the poor knowledge of the stress field has an important impact on the design and development of engineered geothermal energy systems in the Canadian off-grid community of Kuujjuaq.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.037
GPT teacher head0.228
Teacher spread0.191 · 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 designSimulation or modeling
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

Citations9
Published2023
Admission routes3
Has abstractyes

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