Re-interpreting renewable and non-renewable water resources in the over-pressured Pannonian Basin
Bibliographic record
Abstract
With climate change, population growth and the resulting escalating water shortage, humanity is increasingly turning to non-renewable and even fossil groundwater resources, which poses a major challenge to sustainable water management. In this study, 2D basin-scale numerical simulations were carried out on the COMSOL Multiphysics ® finite element numerical platform to identify non-renewable water resources in the Central Pannonian Basin (Central Europe, Hungary) based on the lack of hydraulic connection to recharge areas. The concept and boundary conditions (fixed water table configuration at the top, pressure-elevation profiles on the lateral sides, and constant pressure on the bottom) were derived from a previous basin-scale hydraulic data evaluation study, while the hydrostratigraphic subdivision was based on seismic and well log interpretations. As a result, topography-driven groundwater flow systems fed by meteoric water infiltration were separated from a transition zone, which contains non-renewable groundwater resources and covers 85% area of the simulated 110 km long and roughly 1600 m deep cross-section what was previously thought to be fully renewable. Such complex flow pattern and re-interpretation of the renewable and non-renewable groundwater resources can be expected in any terrestrial sedimentary basin with over-pressured flow domains.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".