Caravan-Qual: A global scale integration of water quality observations into a large sample hydrology dataset
Bibliographic record
Abstract
Caravan-Qual is an open access dataset that brings water quality to the research paradigm of large sample hydrology (LSH), integrating daily water quality data from 100 constituents with catchment attributes, meteorological forcing and co-located streamflow observations. Water quality data in Caravan-Qual is compiled from several existing global, regional and national databases, all of which have fully open-access licenses that permit redistribution, including: • Global: UNEP GEMS/Water Global Freshwater Quality Archive (GEMS) • Global: Global River Water Quality Archive (GRQA) • Global: GLObal River Chemistry (GLORICH) dataset • Europe: NORMAN EMPODAT • Europe: Waterbase WISE State of Environment (Waterbase) • United States: Water Quality Portal (WQP) • China: China National Environmental Monitoring Centre (CNEMC) • United Kingdom: Department for Environment, Food and Rural Affairs (UK-EA) • Canada: Canadian Environmental Sustainability Indicators (CESI) • Switzerland: National Surface Water Quality Monitoring Programme (NAWA) Where possible, water quality monitoring observations are matched to publicly accessible streamflow observations (Caravan + extensions), and stream attributes (GEOGLOWSv2), catchment attributes (HydroATLAS) and climate data (ERA5-Land) are derived. Additional resources ⦁ Code for (re-)creating or extending Caravan-Qual at: https://github.com/SustainableWaterSystems/Caravan-Qual/ ⦁ Jupyter notebook for exploring Caravan-Qual at: https://github.com/SustainableWaterSystems/Caravan-Qual/tree/main/example ⦁ Lightweight version of Caravan-Qual: https://zenodo.org/records/17787066
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.022 |
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".