Bibliographic record
Abstract
Urban runoff models require the detennination of excess rainfall for pervious areas in catchments.This is generally achieved by means of infiltration equations such as, for example, the Horton and Green-Ampt equations in the SWMM andMIDUSS models.Some models (MIDUSS, OTTHYMO) also use the SCS runoff curve number method.Computation of the excess rainfall by any of these methods requires the estimation of two or three parameters which are generally functions ofthe land use and soil properties, including the moisture content of the pervious areas.Values of the parameters can be obtained by infiltrometer measurements, but field measurements are time-consuming and may not be practical.Typical parameter values arc available in the literature but, in many cases, modelers use optimization techniques to estimate the "best" parameter values during the calibration of the model.Ideally, the paran1eter optimization should be guided by parameter values derived for the specific catchment from observed rainfall-runoff data, but independently of the calibration process.In this chapter an asymptotic method of estimating the curve number CN and initial abstraction Ia for a watershed, from observed rainfall and runoff events, is presented.The proposed method is asymptotic in the sense that the estimated values approach the "true" values as the number of observations increases.The method provides estimates of spatially averaged CN and I" for each event for the catchment area upstream of the streamflow gauging station.Muzik, I. 2003."Curve Numbers in Stormwater Runofi Simulation."
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".