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Record W4405999498 · doi:10.1016/j.renene.2024.122324

A thermal power budget approach to evaluate the geothermal potential of a flooded open-pit mine: Case studies from the Carey Canadian and King-Beaver mines (Canada)

2025· article· en· W4405999498 on OpenAlexafffundabout
Samuel Lacombe, Félix-Antoine Comeau, Jasmin Raymond

Bibliographic record

VenueRenewable Energy · 2025
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologies
KeywordsGeothermal gradientBeaverEnvironmental scienceMining engineeringGeology

Abstract

fetched live from OpenAlex

Following mine closure, open-pit mines gradually fill with ground and surface water. Due to its thermal inertia, this water maintains a relatively stable temperature year round, making it suitable for heating and cooling buildings. Previous estimates of the geothermal potential of a flooded open-pit mine primarily focused on the water volume alone, often underestimating the total potential by neglecting heat exchanges with the surrounding rock and incoming water. This paper introduces a novel analytical approach based on an improve thermal power balance concept to better estimate the geothermal potential of a flooded open-pit mine. Over a 25-years analysis, it was shown that the host rock can contribute over 15 % of the thermal energy in the water, while water supply can double this energy. The method developed is both quick and reliable, allowing for early stage evaluation of geothermal resources by accounting not only for the mine's water volume but also energy inputs from precipitation, runoff, groundwater recharge and the host rock. The study focuses on the Carey Canadian and King-Beaver open-pits, two closed asbestos mines in southern Quebec (Canada).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.834
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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.

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

Citations3
Published2025
Admission routes3
Has abstractyes

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