Use of Regional Scale Field and Modelling Methods to Evaluate Nearshore Groundwater Discharge to a Large Glacial Lake
Bibliographic record
Abstract
Groundwater discharge may be an important pathway for delivering pollutants to large lakes, but this pathway is poorly understood. Understanding the potential for groundwater discharge to deliver pollutants to lakes requires an evaluation of the magnitude and spatial variability of groundwater discharge to the lake, and the history of the discharging groundwater. The first objective of this thesis was to evaluate and quantify the spatial variability of groundwater discharge to a large glacial lake, Lake Simcoe, Ontario, using the naturally occurring radon isotope tracer (222Rn). Regional scale boat surveys were conducted along 80% of the Lake Simcoe shoreline using portable radon detection equipment. Groundwater discharge hotspot areas were identified based on spatial variability in lake water 222Rn concentrations, and regional hydrogeological features were linked to these hotspot areas to develop broadly applicable understanding of the observed spatial distribution of groundwater discharge. The second objective of this thesis was to compare 222Rn-derived to model simulated estimates of groundwater discharge in two areas along the Lake Simcoe shoreline. This comparison built further confidence in the groundwater discharge estimates, and enabled the strengths and limitations of each method to be assessed. Particle tracking analysis was used to evaluate the history of groundwater discharging along the northwestern shoreline of Lake Simcoe, and the potential implications for lake water quality in this area. The findings of this thesis provide broadly applicable knowledge needed to focus efforts aimed at managing non-point pollution sources to large glacial lakes including groundwater discharge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".