HydroBudget – Groundwater recharge model in R
Bibliographic record
Abstract
HydroBudget (HB) is a spatially distributed groundwater recharge (GWR) model that computes a superficial water budget on grid cells with outputs aggregated into monthly time steps. It was developed as an accessible and computationally affordable model to simulate GWR over large areas (thousands of km2, regional-scale watersheds) and for long time periods (decades), in cold and humid climates. The model is coded in R and was developed at UQAM by the team of Pr Marie Larocque’s research Chair (Water and land conservation) as part of a project funded by the Quebec Ministry of the Environment (Ministère de l’Environnement et de la Lutte contre les changements climatiques - MELCC). Results of GWR simulation over southern Quebec (Canada) with HB are presented in Dubois et al. (2021). Le model script is provided with an application example for the Petite du Chene River in southern Quebec and a User-guide. As of July 2023, the further development of the HydroBudget model will be included in the rechaRge package. More information can be found here: https://www.epfl.ch/labs/lch/research/water-and-groundwater-management/recharge-an-r-package-for-integrated-groundwater-recharge-modelling-in-r/ Additionally, the development of the code can now be followed here: https://github.com/gwrecharge
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.081 | 0.059 |
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".