Maximize the benefits of water sensitive urban designs in a local government area: Western Australia
Bibliographic record
Abstract
Until recently, stormwater management strategies have failed to adequately consider the criticality of spatially varying soil permeability values and their implications on drainage designs. This case study was carried out in new development areas, 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 was used to investigate the in-situ saturated hydraulic conductivities. Test results were categorized into four main 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), based on the theoretical requirements of stormwater management techniques. Finally, with the help of the existing soil maps, the point represent hydraulic conductivity data were been generalized logically in order to develop the hydraulic conductivity maps representing the areal average as an electronic shape files by using a GIS Arc view mapping software. The future development areas under Central Maddington, Kenwick, Central and Outer Beckenham have been identified as low permeable areas which is not suitable for infiltration based stormwater management strategies whereas the Landford, Thornlie, North Huntingdale and Gosnells has been identified as high permeable areas which is highly recommended for infiltration based stormwater management strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".