Sustainable Drainage System-Capillary Barrier Column Experiment Data
Bibliographic record
Abstract
Dataset to support Canadian Geotechnical Journal article publication entitled 'Performance of sustainable drainage capillary barrier systems for climate change adaptation in temperate climates'. See https://doi.org/10.1139/cgj-2024-0368Capillary barrier systems (CBS) offer a sustainable solution supporting sustainable drainage systems (SuDS) to prevent urban flooding: The occurrence of flooding in urban areas is increasing in response to more intense precipitation, changes in land use that increase runoff (e.g., reduction of green spaces), and reduced water retention of soils. The need for adaptation to the impacts of extreme weather extends to buried assets (e.g., utilities, pavement subbases, and foundations) that are vulnerable to deterioration due to shrink–swell behaviour. Combined SuDS and capillary barriers offer a solution to these challenges. Here, small-scale (110 mm diameter, 1 m length) column experiments were used to test capillary barrier systems, also modelled in HYDRUS 1-D, to consider the impact of relative grain size between the two constituent materials, the use of geosynthetic filter fabrics, and the thickness of the water retention layer on combined sustainable drainage-capillary barrier system (SuDS-CBS) performance under a range of storm inflows. Recycled materials including crushed concrete and water treatment residual (a waste product of the water treatment industry) are shown to be effective for use in SuDS-CBS. Laboratory experiments and numerical modelling demonstrate the importance of antecedent moisture conditions for determining the performance of a SuDS-CBS during rainstorm events.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.013 |
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".