Hydraulic and hydrological data from surface and subsurface soils across the Thames catchment, UK, 2021
Bibliographic record
Abstract
This dataset contains information about surface and sub-surface hydraulic and hydrological soil properties across the Thames (UK) catchment. Soil dry bulk density, estimated soil porosity, soil moisture and soil moisture retention (to 100 cm suction) were determined through laboratory analysis of soil samples collected at five depths between the surface and 100 cm below ground level (where possible). Surface soil infiltration rates were measured, and soil saturated hydraulic conductivity was calculated at 25 cm and 45 cm depths (where possible). Field scale point data were collected at seven sites in the Thames Catchment, with three sub-groups of sites under different land use and management practices. The first land management group included three arable fields in the Cotswolds, Gloucestershire, on shallow soils over Limestone with no grass in rotation, herbal leys in rotation or rye and clover in rotation. The second group included two arable fields in near Wantage, Oxfordshire, on free draining loamy soils over chalk with conventional management or controlled traffic. The final group included a permanent grassland and broadleaf woodland on slowly permeable soil over mudstone near Oxford, Oxfordshire. Data were collected in representative infield areas; trafficked areas (e.g. tramlines or animal tracks), and untrafficked margins. Samples and measurements were taken between April 2021 and October 2021, with repeats taken before and after harvest. Soil samples were collected using Eijkelkamp 07.53.SC sample ring kit with closed ring holder and the Edelman auger and Stony auger when required. Infiltration measurements were taken using Mini Disk Infiltrometers. Soil saturated hydraulic conductivity was measured using Guelph permeameters. Soil bulk density and porosity were calculated using oven drying methods. Soil moisture retention was calculated using an Eijelkamp Sandbox. This dataset was collected by UKCEH as part of the 'Land management in lowland catchments for integrated flood risk reduction' (LANDWISE) project. LANDWISE seeks to examine how land use and management can be used to reduce the risk of flooding for communities. LANDWISE is one of three projects comprising the Natural Environment Research Council Natural Flood Management Research Programme. The work was supported by the Natural Environment Research Council Grant NE/R004668/1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.101 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.025 | 0.154 |
| Research integrity | 0.001 | 0.024 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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; both teacher heads agree on what is shown here.
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".