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Record W3142276836 · doi:10.1111/gwat.13100

Borehole Heat Exchangers—Addressing the Application Gap with Groundwater Science

2021· article· en· W3142276836 on OpenAlexaff
Robert A. Schincariol, Jasmin Raymond

Bibliographic record

VenueGround Water · 2021
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsInstitut National de la Recherche ScientifiqueWestern University
Fundersnot available
KeywordsGeothermal gradientBoreholeHeat pumpGroundwaterHeat exchangerGroundwater flowHydrogeologyEnvironmental scienceElectricityCivil engineeringComputer scienceEnvironmental economicsEngineeringMechanical engineeringGeologyAquiferGeophysics

Abstract

fetched live from OpenAlex

Hydrogeologists and mechanical engineers approach the design of geoexchange systems, and the associated borehole heat exchanger (BHE) fields, in different ways, each focusing on their knowledge areas. While these differences have created a strong research base, with well-published innovations and designs that collectively allow for sustainable systems, industry has not embraced these recent advancements. Despite abundant research demonstrating how complex shallow groundwater flow and temperature conditions can influence BHE design and operation, the low-temperature geothermal industry remains largely fixed on simple analytical codes and assumed uniform ground conditions. Geoexchange system inefficiencies become masked via reduced heat pump performance and increased electricity consumption. Similarly, interactions between BHE fields and infrastructure in urban areas are slow to manifest and are often unrealized due to a lack of field temperature data. While regulations that include hydrogeological factors have been developed in some jurisdictions, they are largely voluntary or rudimentary and can be unapplied in industry. Addressing this application gap may be unreasonable as designing and installing thermally efficient geoexchange systems can put them out of the cost envelope of competing heating and cooling systems. Perhaps for hydrogeologists, the way forward lies in the use of BHE's to facilitate contaminated sites remediation, an area we are familiar with, and one that allows for innovative technologies to reduce cost envelopes. Following that path, hydrogeologists can help improve system efficiencies while fully considering the dynamic nature of advective and thermal transport by groundwater.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.861

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 science0.0000.000
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 head0.040
GPT teacher head0.257
Teacher spread0.217 · 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 designBench or experimental
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

Citations6
Published2021
Admission routes1
Has abstractyes

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