CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. We present the first large-sample catchment hydrology dataset for GreatBritain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sampleStudies). CAMELS-GB collates river flows, catchment attributes and catchmentboundaries from the UK National River Flow Archive together with a suite ofnew meteorological time series and catchment attributes. These data areprovided for 671 catchments that cover a wide range of climatic,hydrological, landscape, and human management characteristics across GreatBritain. Daily time series covering 1970–2015 (a period including severalhydrological extreme events) are provided for a range ofhydro-meteorological variables including rainfall, potentialevapotranspiration, temperature, radiation, humidity, and river flow. Acomprehensive set of catchment attributes is quantified includingtopography, climate, hydrology, land cover, soils, and hydrogeology.Importantly, we also derive human management attributes (includingattributes summarising abstractions, returns, and reservoir capacity in eachcatchment), as well as attributes describing the quality of the flow dataincluding the first set of discharge uncertainty estimates (provided atmultiple flow quantiles) for Great Britain. CAMELS-GB (Coxon et al., 2020;available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9)is intended for the community as a publicly available, easily accessibledataset to use in a wide range of environmental and modelling analyses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it