A Laboratory Rig for Testing Runoff from Paved Surfaces
Bibliographic record
Abstract
When a process is too complex for rigorous mathematical formulation, and simplifying assumptions are introduced for its solution, experimental verification is required, or the approximate numerical model cannot be said to represent the process.Numerical solution of the dynamic wave equations for flow over pavement is such a case.We describe in this chapter a laboratory rig used to test the numerical procedures of the previous chapter (James and Wylie, 2000), and the initial storage theory developed in the previous monograph in this series (James and Johanson, 1999).Our experiments were conducted almost 30 years ago in the Department of Civil Engineering at the University of Natal in Durban.Laboratory conditions were stringently controlled.Experiments are otherwise similar to those recently described by several of the author's graduate students in this series of books.For generality of the mathematical model, the formulation of the wave equations should include a momentum exchange term for rain distributed uniformly over its resulting runoff from the pavement.In the present studies, the mathematical and physical model catchments are also impermeable, to avoid the complexity of infiltration.Under these conditions, the physical and mathematical models may be considered to represent real rain falling on its resultant runoff from an impervious pavement (Amorocho and Hart, 1965).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".