A Framework for Untangling Transient Groundwater Mixing and Travel Times
Bibliographic record
Abstract
Abstract Understanding the mixing between surface water and groundwater as well as groundwater travel times in vulnerable aquifers is crucial to sustaining a safe water supply. Age dating tracers used to infer apparent travel times typically refer to the entire groundwater sample. A groundwater sample, however, consists of a mixture of waters with a distribution of travel times. Age dating tracers only reflect the proportion of the water that is under the dating range of the used tracer, thus their interpretation is typically biased. Additionally, end‐member mixing models are subject to various sources of uncertainties, which are typically neglected. In this study, we introduce a new framework that untangles groundwater mixing ratios and travel times using a novel combination of in‐situ noble gas analyses. We applied this approach during a groundwater pumping test carried out in a pre‐alpine Swiss valley. First, we calculated transient mixing ratios between recently infiltrated river water and regional groundwater present in a wellfield, using helium‐4 concentrations combined with a Bayesian end‐member mixing model. Having identified the groundwater fraction of recently infiltrated river water (Frw) consequently allowed us to infer the travel times from the river to the wellfield, estimated based on radon‐222 activities of Frw. Furthermore, we compared tracer‐based estimates of Frw with results from a calibrated numerical model. We demonstrate (i) that partitioning of major water sources enables a meaningful interpretation of an age dating tracer of the water fraction of interest and (ii) that the streambed has a major control on the estimated travel times.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".