Evaluation of the glacial impact on the shallow heat-flow density in the North German Basin
Bibliographic record
Abstract
Paleoclimatic effects may still influence the present day subsurface temperature distribution and therefore the heatflow density calculated in affected depth levels. Cooling of several degrees Celsius into depths of up to 1.5 – 2km were reported for areas which were strongly affected by the Pleistocene ice ages (e.g. Canada, Poland, andDenmark). However, although this phenomenon is well known, not much research has been performed to quantifythese processes in Northern Germany, an area where Pleistocene ice margins of the last ice ages are located. Tofill that gap we compiled new data from two boreholes in the eastern part of the North German Basin, one locatedbeneath the former ice shield of the last glaciation, and one located in the foreland. We determined thermal rockproperties (thermal conductivity, thermal diffusivity, and specific heat capacity) on drill core samples and used itas calibrator for well-log based calculations of thermal parameter profiles along the borehole. The results wereused for heat-flow computations with depth and implemented as a base for an analytical solution of the heatequation as well as inversion modelling. By showing the discrepancy of observed and theoretical backgroundtemperature and heat flow density profiles, we aim to improve the understanding of the regional thermal responseto the last glaciations.
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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 | 0.000 | 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".