Modelling streamflow depletion under different groundwater pumping scenarios involving the Dalmeny aquifer in Saskatchewan
Bibliographic record
Abstract
In the Dalmeny Basin both ecosystems and people rely on local watercourses so it is important to determine how groundwater pumping could affect streamflow. As such, simulation of how streamflow would deplete under different scenarios was done using parameters within realistic ranges present in the area. From there the upper limit to the pumping rate before significant ecological damage would occur in the North Saskatchewan River, the region's notable watercourse, was determined. The main method was the use of the R package called streamDepletr and its built in Glower, Hunt, and Hantush functions. One notable result is that a streamed with a weighted average composition resulted in the threshold before ecological damage being lower than if it were solely composed of the Upper floral unit. Additionally, the system is most sensitive to variations in storativity. In comparing the Glover, Hunt, and Hantush methods, it was also discovered that for identical scenarios, the Glover method predicts the most stream depletion white the Hantush method predicts the least. It was determined that to surpass the significant ecological damage threshold, the pumping rate from the Dalmeny Aquifer would have to surpass its recharge rate. Practically, reaching this point is unnecessary given the area's current and historical groundwater usage, as well as unsustainable for the aquifer itself.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".