Modeling St. John River (N.B., Canada) incomplete hydrometric data using bivariate distributions
Bibliographic record
Abstract
This study deals with incomplete bivariate data in hydrology, where information contained in a hydrological series of relatively long length (X, the auxiliary variable) is utilized to enhance the quality of the quantile estimates for a series of shorter length (Y, the variable of main interest), when there is an association between X and Y. It is suggested that bivariate models for representing (X, Y) be constructed by means of copulas, which allows for flexibility in choosing both the marginals and the bivariate distributions. Parameter estimation is done by maximum likelihood (ML), where all the unknown parameters of the bivariate model are estimated simultaneously. A case study using flow records at three gauging stations on the St. John River (New Brunswick, Canada) is used to demonstrate the interest of using bivariate distributions for modeling incomplete data. By using (X, Y) bivariate data observed on the St. John River, the probability density function (pdf) obtained from a univariate frequency analysis of Y (Model A), is compared to the pdf constructed using a bivariate model relating X to Y (Model B). It is shown that Model B reduces the variability in the Y pdf as compared to the pdf obtained from Model A, and also corrects the quantile estimates for Y through a location shift.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".