Estimating Ground Water Flux into Large Lakes: Application in the Hamilton Harbor, Western Lake Ontario
Bibliographic record
Abstract
Abstract Over the last two decades, there has been a growing recognition of the importance of evaluating ground water's contribution to lakes. As a result, a number of techniques have been developed for measuring hydraulic properties across lake bottoms, primarily in the littoral zone. However, for larger, deeper lakes such as the Great Lakes these techniques are impractical in the profundal zone. As a result, many water balance calculations in these settings omit the ground water component altogether owing to the difficulties encountered in making the necessary hydraulic observations across the deeper lake bottom sediments. In this study, a methodology is developed for determining the flux into large, deep lakes using a combination of existing and recently developed techniques. The methodology is applied to the Hamilton Harbor, a natural bay at the western end of Lake Ontario, to estimate the ground water contribution to the harbor's water budget. Hydraulic gradients were monitored in 37 piezometers within the harbor during 1993 and 1994. Calculated hydraulic gradients, along with sediment hydraulic conductivities measured or estimated using a number of techniques, are used to estimate ground water flux to the harbor through Thiessen polygon weighting. Measured hydraulic gradients ranged from −0.333 to 0.430, the majority being upward indicating ground water discharge conditions. Gradients were varied across the harbor and increased in magnitude closer to shore. The total ground water contribution to the harbor was estimated to be 2.1 × 10 7 m 3 /yr. Compared with other hydrological components, ground water was slightly larger than the yearly precipitation input, and approximately 8 % of the total surface inflows to the harbor, and 2 % of the total surface outflow through the Burlington ship canal, which connects the harbor to Lake Ontario. The computed ground water flux suggests that despite the fact that ground water flux to large lakes may typically be a smaller input than surface water inputs, it is still a significant component of the overall water budget and should not be automatically omitted from water balance calculations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".