Modelling Low Impact Development Potential With Urban Hydrological Response Units
Bibliographic record
Abstract
The purpose of this thesis is to develop a methodology for hydrological modelling the performance of Low Impact Development technologies using an Urban Hydrological Response Unit approach. The K-Means Cluster Analysis procedure was carried out to create clusters of lot parcels which represented the Urban Hydrological Response Units. Different sampling methods were used to select lots from each of the clusters to model before and after Low Impact Development implementation. The runoff response (m3) of an approximate final cluster centre was used to calculate the total runoff (m3) of each cluster. After adding the total runoff (m3) for a group of 15 clusters, the benchmark runoff value (m3) from modelling all lots was closely approached with and without Low Impact Development. A random sample of 7 % and 90 % of lots from each cluster for a group of three clusters closely approached the benchmark runoff value (m3) for both no Low Impact Development and Low Impact Development respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".