Groundwater Recharge Assessment in the Chateauguay River Watershed
Bibliographic record
Abstract
The objective of this study was to evaluate groundwater recharge in the Chateauguay River watershed. The Hydrologic Evaluation of Landfill Performance (HELP) model was used to assess daily values of recharge, evapotranspiration and runoff. The study area was divided into a regular grid, 250 m × 250 m, for a total of 47,616 grid elements. The input parameters included soil physical properties, land use, vegetation and climate data. Calibration of HELP was carried out against runoff and baseflow estimates obtained from separation of five river hydrographs. Over a 39 year period, the mean annual recharge rate was estimated at 86 mm, or 9% of the total precipitation. Areas characterized by high water level elevations and unconfined flow conditions were identified as the main recharge areas. Daily estimates show that recharge takes place mainly in spring and fall. Over the observed period, the annual variations of evapotranspiration and runoff were directly related to changes in precipitation, whereas the annual recharge response was subdued, with much lower variations. HELP was also used to assess potential climate change scenarios using data for the driest and most humid years. The mean annual recharge was 51 mm for the driest year and 99 mm for the most humid year. Differences in the spatial distribution of recharge for the predictive scenarios indicate that the areas most sensitive to climate change correspond to the preferential recharge areas.
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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 | 0.001 | 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".