Revisiting the historical tritium levels in precipitation in Greece – Preliminary assessment of groundwater transit times
Bibliographic record
Abstract
H), is very often used as a dating tool in hydrogeological studies, since it enters the water cycle as part of the water molecule through precipitation. However, the assessment of groundwater transit times and recharge often requires knowing the local historical records of tritium levels in precipitation during the previous seven decades, or the tritium in precipitation (TIP) time series. Here, we compiled all tritium records in precipitation in Greece, with the majority of stations showing sporadic measurements, with the aim of reconstructing a TIP for groundwater dating purposes. The monitoring station of Vienna proved to be more reliable for filling the gaps in the time series than the Ottawa station. Conventional methods to fill the TIP gaps, such as the correlation method (CM), were compared with more advanced machine learning tools, such as the Convolutional Neural Networks (CNN). The comparison showed that the artificial Intelligence Method (AIM) performed best, due to its ability to capture complex nonlinear trends that are usually inherent in real-world data. The preliminary qualitative assessment of groundwater tritium data from Greece, in comparison to the TIP, showed the occurrence of groundwaters of mean transit times ranging from a few years to decades. Better incorporation of groundwater transit times and recharge rates into the study of aquifer systems is essential for developing strategies for sustainable water management in Greece and worldwide.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".