Regional effects of paleoclimate history on the subsurface temperature distribution in Germany
Bibliographic record
Abstract
Knowledge of the underground temperature distribution is crucial for evaluating geothermal potential and ensuring the long-term safety of heat-producing waste in repositories. Previous research, mainly conducted in Northern Europe and Canada, has shown that the Pleistocene Glaciations have an additive effect, resulting in a cooling of several degrees Celsius at depths of up to two kilometers. Recent studies indicate that the Last Glacial Period and the recent warming of the past 100–150 years have the greatest paleoclimatic impact on the current shallow to medium depth subsurface temperature distribution in Germany. If thermophysical properties of the subsurface are known, the distribution of underground temperatures can also be used to reconstruct the local ground surface temperature history using borehole climatology. Ground surface temperature reconstructions have low temporal resolutions, but they are directly reconstructed from temperature measurements without the use of climate proxies. Observations of the subsurface temperature distribution are limited to boreholes that are undisturbed by drilling or operations like production tests. Furthermore, the coupling of ground surface temperatures and surface air temperatures presents a significant challenge due to complex and transient surface processes associated with soil types, precipitation, vegetation, and the distribution of water bodies and glaciers. A systematic study of the paleoclimatic impact on the subsurface temperature distribution in sedimentary regions in Germany has not yet been conducted. Moreover, borehole climatology studies in Canada and Northern Europe has mainly concentrated on local reconstructions of ground surface temperatures, focusing on single or a limited number of boreholes. The aim of this study is to investigate the paleoclimatic effect of the Holocene on the subsurface temperature distribution in Germany and to quantify regional variations in the ground surface temperature histories. To achieve this, we have identified wells in sedimentary regions across the country that satisfy the prerequisites for borehole climatology. By using geophysical well logs, we derive the thermophysical characterization of the subsurface. We are examining the continuous temperature profiles to determine the magnitude, and regional variability of the Holocene paleoclimatic signal in borehole temperature profiles throughout Germany.
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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 | 0.001 | 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".