rechaRge – a package for integrated groundwater recharge modelling in R
Bibliographic record
Abstract
[9:56 AM] Emmanuel DuboisThe project introduces the new R package, rechaRge, dedicated to open-source groundwater recharge (GWR) models. The goal is to facilitate the simulation of GWR estimates for researchers, professionals, and stakeholders, for both hydrogeologists and non-hydrogeologists, by providing all the tools for state-of-art modelling and the available GWR models in a single R package. The package includes functions for data preparation (utility functions), automatic calibration, sensitivity analysis, and uncertainty analysis, all integrated directly in the R environment. A first open-source GWR model, the HydroBudget model, is also incorporated in the package. The model’s excellent performance allowed for the simulation of large datasets of spatially distributed and transient GWR in several projects in Canada, ranging from small watershed scale (few km2) to regional scale (thousands of km2). Sensitivity analysis, calibration, and uncertainty for the models were greatly facilitated by the utility functions of the package. At the region scale, GWR was simulated within a global change context with a spatial resolution of a 500 m x 500 m and a monthly time step for up to 150 years and 24 scenarios. Moreover, the rechaRge package is a collaborative effort and developers of open-source GWR modelling codes are warmly invited to publish their models in this package and take advantage of the existing functions.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.197 | 0.157 |
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".