Hydrology Tool Set (HTS): A Suite of Online Tools and Models for Open Science
Bibliographic record
Abstract
Hydrology Tool Set (HTS; http://www.hydrotools.tech) integrates a suite of free online hydrology tools and models. These can be used for advancing the understanding of local and watershed scale hydrological processes and can be employed in many areas of environmental research, such as the assessment of the impact of agricultural practices, urbanization, climate change, etc. All HTS tools and models are free to use and do not require user registration. HTS currently includes SepHydro (surface runoff and groundwater contributions to streamflow via hydrograph separation), ETCalc (evapotranspiration forms), SWIB (crop water deficit and excess, irrigation requirement and its impact on aquifer storage), SNOSWAB (snowpack processes, soil water content, soil water balance), TotPrePart (precipitation partitioning) and GWRech (groundwater recharge). These tools and models have been developed through a collaborative research effort of Environment and Climate Change Canada (ECCC), Agriculture and Agri-Food Canada (AAFC), Canadian Rivers Institute (CRI) and the University of New Brunswick (UNB). All the tools and models operate using daily datasets; have a user-friendly interface, with streamlined and easy to follow procedures; require minimal input data and provide flexibility with respect to the choice of methods, settings, displaying and exporting of data via plots and tables. The tools use a daily time step input and can be applied to any location for which data is available. The HTS tools and models can be used for scientific purposes, either for independent analyses or for providing input to more complex models, as well as for educational purposes, since each application includes a user guide and a sample data set, which can be used for familiarizing with the tool operation and/or for providing basic knowledge regarding key hydrological cycle components and processes. As of July 2025, ~60,000 HTS analyses have been conducted by users from around the world.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.041 |
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".