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Record W2015165704 · doi:10.1111/gwat.12162

Screening for Heat Transport by Groundwater in Closed Geothermal Systems

2014· article· en· W2015165704 on OpenAlexafffund
Grant Ferguson

Bibliographic record

VenueGround Water · 2014
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydraulic conductivityThermal conductionGeothermal gradientGroundwater flowGroundwaterAdvectionGeologyAquiferGroundwater modelPéclet numberGeotechnical engineeringSoil scienceThermal conductivityHeat transferMetamorphic rockPetrologyMechanicsGeophysicsSoil waterMaterials scienceThermodynamics

Abstract

fetched live from OpenAlex

Heat transfer due to groundwater flow can significantly affect closed geothermal systems. Here, a screening method is developed, based on Peclet numbers for these systems and Darcy's law. Conduction-only conditions should not be expected where specific discharges exceed 10(-8) m/s. Constraints on hydraulic gradients allow for preliminary screening for advection based on rock or soil types. Identification of materials with very low hydraulic conductivity, such as shale and intact igneous and metamorphic rock, allow for analysis with considering conduction only. Variability in known hydraulic conductivity allows for the possibility of advection in most other rocks and soil types. Further screening relies on refinement of estimates of hydraulic gradients and hydraulic conductivity through site investigations and modeling until the presence or absence of conduction can be confirmed.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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 designObservational
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

Citations24
Published2014
Admission routes2
Has abstractyes

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