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Record W3112751983 · doi:10.3390/su122410613

Earth–Air Heat Exchanger Geo-Climatic Suitability for Projected Climate Change Scenarios in the Americas

2020· article· en· W3112751983 on OpenAlexafffund
Andrew Zajch, William A. Gough, Giacomo Chiesa

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsRepresentative Concentration PathwaysEnvironmental scienceClimate changeClimatologyClimate modelHeat exchangerGlobal warmingGeneral Circulation ModelAtmospheric sciencesEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Earth–air heat exchangers (EAHE) provide heating and cooling that is intrinsically tied to the climate of the surrounding environment. A climate-based approach was applied to 273 sites for both historical and projected climate conditions, with the latter being defined by three different Representative Concentration Pathways (RCPs) from the CMIP5 collection of Global Circulation Models (GCMs). Changes to heating and cooling degree hours as well as heating and cooling capacity were estimated and used to classify geo-climatic suitability. The analysis revealed cooler climates will retain their ability to provide cooling despite increasing cooling needs driven by warming temperatures. On the other hand, warmer, more tropical, climates will observe reduced suitability as cooling demand grows. The magnitude and variability of the changes in EAHE potential were greatest for the RCP8.5 scenario during the 2061–2090 time period, particularly for regions with a comparable mix of heating and cooling needs. Ultimately, the results demonstrate that future EAHE suitability is climate dependent, with cooler climates being relatively resistant to changes when compared to warmer climates. The results can be used by stakeholders to find useful climate analogs for their sites of interest to consider the potential impact of global climate change on EAHE usability.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.396
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.289
Teacher spread0.253 · 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 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

Citations13
Published2020
Admission routes2
Has abstractyes

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