Determination of groundwater discharge rates and water residence time of groundwater‐fed lakes by stable isotopes of water (<sup>18</sup>O, <sup>2</sup>H) and radon (<sup>222</sup>Rn) mass balances
Bibliographic record
Abstract
Abstract Lacustrine groundwater discharge (LGD) and the related water residence time are crucial parameters for quantifying lake matter budgets and assessing its vulnerability to contaminant input. Our approach utilizes the stable isotopes of water (δ 18 O, δ 2 H) and the radioisotope radon ( 222 Rn) for determining long‐term average and short‐term snapshots in LGD. We conducted isotope balances for the 0.5‐km 2 Lake Ammelshainer See (Germany) based on measurements of lake isotope inventories and groundwater composition accompanied by good quality and comprehensive long‐term meteorological and isotopic data (precipitation) from nearby monitoring stations. The results from the steady‐state annual isotope balances that rely on only two sampling campaigns are consistent for both δ 18 O and δ 2 H and suggested an overall long‐term average LGD rate that was used to infer the water residence time of the lake. These findings were supported by the good agreement of the simulated LGD‐driven annual cycles of δ 18 O and δ 2 H lake inventories with the observed lake isotope inventories. However, radon mass balances revealed lower values that might be the result of seasonal LGD variability. For obtaining further insights into possible seasonal variability of groundwater–lake interaction, stable water isotope and radon mass balances could be conducted more frequently (e.g., monthly) in order to use the derived groundwater discharge rates as input for time‐variant isotope balances.
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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 | 0.000 | 0.000 |
| 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".