Improvements in Urban Sub-Catchment Runoff Modeling
Bibliographic record
Abstract
This chapter descnbes the development of an improved sub--catchment runoff modeling technique for urban catchments to help overcome problems of parameter scaling and process lumping inherent in many existing schemes. The development is based on five years of detailed monitoring of a typical urban catchment in Canberra, Australia utilising nested rainfall and flow gauges to characterise the accumulation of runoff throughout the catchment during a wide range of storm events. The gauging network provided data to interpret lot scaled process units and their accumulation throughout the 90 hectare (ha) catchment. The detailed rainfall/runoff data led to a modified modeling approach that incorporated lot scaled process definition and the means to cumulate these to any size catchment. Independent testing of the procedure was carried out on a separate catchment in Sydney. The approach also provided the means to further test different lot scale drainage provisions and their effect at different catchment scales.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".