Review of the Heat Flow Mapping in Polish Sedimentary Basin across Different Tectonic Terrains
Bibliographic record
Abstract
Heat flow patterns variability related to the age of the consolidated, and differences in, sedimentary thickness of the sedimentary succession are important constraints upon the thermal state of the sedimentary fill and its geothermal energy potential. Heat flow in the Permian basin of central Europe varies from a low of 40 mWm−2 in the Precambrian Platform to 80 mWm−2 in the Paleozoic basement platform influencing temperature for geothermal potential drilling depth. Continuity of thermal patterns and compatibility of heat flow Q across the Permian basin across the Polish–German basin was known from heat flow data ever since the first heat flow map of Europe in 1979. Both Polish and German heat flow determinations used lab-measured thermal conductivity on cores. This is not the case for the recent heat flow map of Poland published in 2009 widely referenced in Polish geological literature. Significant differences in heat flow magnitude exist between many historical heat flow maps of Poland over the 1970s–1990s and recent 21st century patterns. We find that the differences in heat flow values of some 20–30 mWm−2 in Western Poland exist between heat flow maps using thermal conductivity models using well log interpreted mineral and porosity content and assigned world averages of rock and fluid thermal conductivity versus those measured on cores. These differences in heat flow are discussed in the context of resulting mantle heat flow and the Lithosphere-Asthenosphere Boundary depth modelled differences and possible overestimates of deep thermal conditions for enhanced geothermal energy prospects in Poland.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".