Risk Assessment Using A Generalized Pareto-based Bivariate Model
Bibliographic record
Abstract
In hydrology, one often has to study the joint probabilistic behavior of two or more variables characterizing a water system. A model constructed from generalized-Pareto-distributed (GPD) marginals is presented in the present paper to study the relation between two variables, The GPD model is the most popular one used in the ‘Peaks-Ove~-Threshold’(POT) approach for studying hydrological extremes, and is also applicable in the ‘Dejicit-Below-Threshold’ (DBT) approach for modeling low extremes. The bivariate model presented herein, allows for risk assessments and calculations associated with each hydrological variable separately, with one variable conditioned by the other, or with both variables taken together, It will be used to model the joint distribution of the duration (XJ and the peak discharge above a threshold (X2) of a real flood series derived from hydrometric data of the Little Southwest Miramichi River, in New Brunswick, Canada.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".