Evaluating lacustrine groundwater discharge to a large glacial lake using regional scale radon‐222 surveys and groundwater modelling
Bibliographic record
Abstract
Abstract Lacustrine groundwater discharge (LGD) can be an important pathway for delivering pollutants to lakes but this pathway is often poorly characterized. Evaluating the potential impact of LGD on lake water quality requires understanding the magnitude and spatial variability of LGD, as well as understanding the age and flow paths of the discharging groundwater (e.g., recharge area, groundwater flow paths, and travel times). This study first compares LGD rates along two ~40 km shoreline lengths of a large glacial lake, Lake Simcoe, Canada, that were independently estimated via a radon‐222 ( 222 Rn) field survey and via regional scale groundwater‐surface water modelling. Backward particle tracking analysis is then used to examine the age and flow paths of the LGD and thereby assess the potential for the LGD to deliver anthropogenic pollutants to the lake. The field and modelling results compare well with respect to the magnitude and spatial variability of LGD. However, the comparison highlights the need for well‐defined hydrogeological characterization if regional scale models are to be applied for LGD estimation. The particle tracking analysis indicates large variation in the groundwater flow path lengths and travels times (>1000 years to <50 years) for LGD along the shoreline. This illustrates that the LGD along different shoreline areas has varying potential to deliver anthropogenic pollutants to the lake. The study findings demonstrate the benefits of comparing independent field measured and model‐simulated LGD estimates, and moreover suggest that it may be possible, in some cases, to use existing regional scale groundwater‐surface water models, purpose‐built for other water resource and quality objectives, to conduct preliminary evaluation of LGD contributions to lakes. Preliminary model‐based evaluation would enable field efforts aiming to quantify and manage LGD to be better targeted rather than relying solely on regional scale field techniques that are often highly resource intensive.
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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 | 0.001 |
| 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".