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Record W4288729422 · doi:10.1088/1748-9326/ac7e60

The evolution of the geothermal potential of a subsurface urban heat island

2022· article· en· W4288729422 on OpenAlexaff
Hannes Hemmerle, Grant Ferguson, Philipp Blum, Peter Bayer

Bibliographic record

VenueEnvironmental Research Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsUniversity of Saskatchewan
FundersDeutsche Forschungsgemeinschaft
KeywordsUrban heat islandAquiferGeothermal gradientGroundwaterEnvironmental scienceGeothermal heatingGeothermal energyResource (disambiguation)Earth scienceHydrology (agriculture)Water resource managementGeologyGeographyMeteorologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Meeting the rising energy demands of cities is a global challenge. Exploitation of the additional heat in the subsurface associated with the subsurface urban heat island (SUHI) has been proposed to address the heating demands. For the sustainable use of this heat it is crucial to understand how SUHIs evolve. To date, there have been no comprehensive studies showing how temperature anomalies beneath cities change over time scales of decades. Here, we reveal the long-term increase of temperatures in the groundwater beneath Cologne, Germany from 1973 to 2020. The rise in groundwater temperature trails atmospheric temperature rise in the rural areas and exceeds the rise in atmospheric temperature in the urban center. However, the amount of heat that is currently stored each year in the thin shallow aquifer reaches only 1% of the annual heating demand. The majority of the anthropogenic heat passes by the vertical extent of the aquifer or is discharged by the adjacent river. Overall the geothermal resource of the urban ground remains largely underused and heat extraction as well as combined heating and cooling could substantially raise the geothermal potential to supply the city’s demand.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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.

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

Citations33
Published2022
Admission routes1
Has abstractyes

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