Global patterns of shallow groundwater temperatures
Bibliographic record
Abstract
Only meters below our feet, shallow aquifers serve as sustainable energy source and provide freshwater storage and ecological habitats. All of these aspects are crucially impacted by the thermal regime of the subsurface. Due to the limited accessibility of aquifers however, temperature measurements are scarce. Most commonly, shallow groundwater temperatures are approximated by adding an offset to annual mean surface air temperatures. Yet, the value of this offset is not well defined, often arbitrarily set, and rarely validated. Here, we propose the usage of satellite-derived land surface temperatures instead of surface air temperatures. 2 548 measurement points in 29 countries are compiled, revealing characteristic trends in the offset between shallow groundwater temperatures and land surface temperatures. Here it is shown that evapotranspiration and snow cover impact on this offset globally, through latent heat flow and insulation. Considering these two processes only, global shallow groundwater temperatures are estimated in a resolution of approximately 1 km × 1 km. When comparing these estimated groundwater temperatures with measured ones a coefficient of determination of 0.95 and a root mean square error of 1.4 K is found.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".