Bivariate flood frequency analysis: Part 1. Determination of marginals by parametric and nonparametric techniques
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In flood frequency analysis, a flood event is mainly characterized by peak flow, volume and duration. These three variables or characteristics of floods are random in nature and mutually correlated. In this article, an effort is made to find out appropriate marginal distribution of the flood characteristics considering a set of parametric and nonparametric distributions, and further mathematically model the correlated nature among them. A set of parametric distribution functions and nonparametric methods based on kernel density estimation and orthonormal series are used to determine the marginal distribution functions for peak flow, volume and duration. In conventional methods of flood frequency analysis, the marginal distribution functions of peak flow, volume and duration are assumed to follow some specific parametric distribution function. The present work performs a better selection of marginal distribution functions for flood characteristics as both parametric and nonparametric estimation procedures are extensively followed. The methodology is demonstrated with 70‐year stream flow data of Red River at Grand Forks of North Dakota, USA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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