Numerical Simulation of Groundwater Flow in the Chateauguay River Aquifers
Bibliographic record
Abstract
The Chateauguay River watershed extends over northeastern New York State (USA) and southwestern Quebec (Canada). Fractured sedimentary rocks of the St. Lawrence Platform host the regional aquifers. Quaternary sediments of variable thickness of up to 45 m overlie the bedrock. The geometric mean hydraulic conductivity of the bedrock aquifers obtained from 548 field measurements is 5.1 × 10–5 m/s with a standard deviation of 0.7 of the logarithms. The modelled area extends from the foothills of the Adirondacks to the St. Lawrence River and covers 2,850 km2. The numerical groundwater flow model was developed using the finite element simulator FEFLOW. The model has 13 layers with layer thicknesses ranging from 5 m for the top layer to 75 m for the bottom layer. The average thickness of the numerical model is 655 m, for a total volume of 1,868 km3. The St. Lawrence River is considered as a specified head boundary; the base and other lateral limits are considered as no-flow boundaries, whereas a head and conductivity-dependent boundary is specified along major streams and wetlands. Spatial recharge rate is applied as a specified flux across the top of the model and was fixed during calibration to reduce model uncertainty. Groundwater withdrawal of 34 Mm3/yr is assigned using sinks for major wells and as a uniform negative flux across the top of the model to account for domestic and other diffuse uses. Calibration was carried out against 153 hydraulic head measurements, with horizontal hydraulic conductivity and vertical anisotropy used as calibration parameters. The regional groundwater flow amounts to 268 Mm3/yr: 12.7% is withdrawn for domestic purposes; aquifer contribution to streams and wetlands is 176 Mm3/yr, and 55 Mm3/yr is discharged to the St. Lawrence River. Groundwater flow appears to be controlled by the sub-horizontal bedding planes contributing to relatively high vertical anisotropy.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".