A Procedure to Identify and Rank Rainfall/Runoff Phenomena for the Evaluation of Urban Stormwater Models
Bibliographic record
Abstract
Computer models are needed to predict the effects of changes in land-use and climate within an urban stormwater drainage catchment.As with any computer model result, it is important to clearly state the reliability of the calculations and the modeling assumptions that were used (James, 2005).A common practice that is used to demonstrate the reliability of an urban stormwater model is to compare calculated results to data that are obtained from field measurements within the catchment (often a split-test calibration/verification procedure is employed).This practice is applicable to the analysis of existing drainage catchments using historical weather data but the difficulty with predicting the effects of changes in land-use or climate is that field data on the changed system are not available.Therefore, the predictive capabilities of the model must be validated, which includes establishing the uncertainties in the calculations (O'Connell and Todini, 1996; Ewan and Parkin, 1996).Quantification of these uncertainties is needed so that informed decisions can be made regarding the implementation of a land-use change or how best to mitigate the risk associated with climate change scenarios.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".