An innovative approach to stormwater management accounting for spatial variability in soil permeability
Bibliographic record
Abstract
In the past few years, major flooding incidents have been experienced in Australia. This has resulted in increased concerns for local authorities, environmental institutions and the public, giving management of stormwater a new priority. Stormwater infiltration is one of the best practise methods to operationally and sustainably handle urban drainage. However, until recently, stormwater management strategies have failed to adequately consider the criticality of spatially varying soil permeability and their implications on drainage designs. With a lack of detailed information on local soil properties, it is difficult to assess the adequacy of stormwater retention / detention requirements. This study was carried out in new land development areas of Gosnells in Western Australia, focusing on identification of soil properties and development of a typology of suitable stormwater management strategies with respect to applicable infiltration capacities. The Guelph Permeameter and the falling head methods were used to investigate the in-situ and laboratory saturated hydraulic conductivities. Test results were categorized into four permeability groups; very rapid (> 1.56 m/day), rapid (0.48<1.56 m/day), moderate (0.12<0.48 m/day) and slow (<0.12 m/day). Finally, these four key permeability categories, combined with the scale of application (lot, street, regional) and operational objective (quality, quantity, conservation), enabled the identification of suitable stormwater management approaches. The results of this study will assist land developers, engineering consultants and local authorities to devise locally appropriate, functional and water sensitive drainage approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".