Probabilistic assessment of urban runoff erosion potential
Bibliographic record
Abstract
At the planning or screening level of urban development, analytical modeling using derived probability distribution theory is a viable alternative to continuous simulation, offering considerably less computational effort. A new set of analytical probabilistic models is developed for predicting the erosion potential of urban stormwater runoff. The marginal probability distributions for the duration of a hydrograph in which the critical channel velocity is exceeded (termed exceedance duration) are computed using derived probability distribution theory. Exceedance duration and peak channel velocity are two random variables upon which erosion potential is functionally dependent. Reasonable agreement exists between the derived marginal probability distributions for exceedance duration and continuous EPA Stormwater Management Model (SWMM) simulations at more common return periods. It is these events of lower magnitude and higher frequency that are the most significant to erosion-potential prediction. Key words: erosion, stormwater management, derived probability distribution, exceedance duration.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".