Modelling the impacts of cranberry farms on the hydrologic regimes of the Bécancour River watershed in Québec, Canada
Bibliographic record
Abstract
The Bécancour River watershed, located half way between Montréal and Québec City in the Province of Québec (Canada) and covering a surface area of about 2600 km2, is dominated by forest in the upstream portion and agriculture in the downstream portion. The production of cranberries (Vaccinium macrocarpon) is an important feature of this watershed. This crop not only relies on abundant water resources for frost protection, soil moisture management, and harvest and winter flooding, but also on tiled drainage system which together impact the watershed hydrology and flow patterns. This study aims at modelling the impacts of cranberry farms on the hydrologic regimes of the Bécancour River watershed in Québec, Canada. We dispose of groundwater level and soil tension data at the root zone from two distinct cranberry farms, meteorological data, Ground-penetrating radar (GPR) and stratigraphy data, and LIDAR data collected over a period of 5 years starting in 2014. We setup the hydrological model using the well-known finite-element-based model named FEFLOW to simulate the hydrological behavior of two cranberry farms in the watershed. The preliminary results are promising and demonstrate the potential of the model in a) depicting and understanding hydrological changes in the watershed and b) supporting decision-making regarding water resources management for agricultural production in the region.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".