The evolution of the geothermal potential of a subsurface urban heat island
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".