Scaling model of a rainfall intensity‐duration‐frequency relationship
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Intensity‐duration‐frequency (IDF) relationships are currently constructed based on an at‐site frequency analysis of rainfall data separately for different durations. These relationships are not accurate and reliable since they depend on many assumptions such as distribution selection for each duration; they require a large number of parameters, and are not time‐independent. In this study, scaling properties of extreme rainfall are examined to establish scaling behaviour of statistical non‐central moments over different durations. A scale invariance concept is explored for disaggregation (or downscaling) of rainfall intensity from low to high resolution and is applied to the derivation of scaling IDF curves. These curves are developed for gauged sites based on scaling of the generalized extreme value (GEV) and Gumbel probability distributions. Numerical analysis was performed on annual maximum rainfall series for the province of Ontario, for storm durations of 5, 10, 15, and 30 min (the typical time of concentration for small urban catchments) and 1, 2, 6, 12, and 24 h (the typical time of concentration for larger rural watersheds). Results show that rainfall does follow a simple scaling process. Estimates found from the scaling procedure are comparable to estimates obtained from traditional techniques; however, the scaled approach was more efficient and gives more accurate estimates compared with the observed rainfall total at all stations. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 it