Choice between competitive pairs of frequency models for use in hydrology: a review and some new results
Bibliographic record
Abstract
A group of statistical distributions useful in hydrological frequency modelling are two-parameter distributions with one scale and one shape parameter. Discriminating between pairs of models within this group is of practical interest. The main discrimination tests that have appeared in the literature are reviewed and a broad comparison is undertaken of their ability to correctly identify the distribution within the pair of distributions being studied. An attempt is also made to classify pairs of distributions according to the difficulty of discriminating between them. In addition, several tests are formulated and compared to discriminate between the Weibull and the log-logistic distributions. These tests are also applicable, with the same ability of correctly choosing between the logistic and the extreme value type 1 models (for minima or maxima). A Monte Carlo study identifies three test statistics as the most powerful for correctly selecting between these models: the ratio of maximized likelihood, Anderson-Darling and (modified) Shapiro-Wilk statistics. The third of these test statistics is specifically shown to be advantageous with small samples. A hydrological example shows how this test statistic is used in practice. Editor D. Koutsoyiannis; Associate editor K. Hamed Citation Ashkar, F. and Aucoin, F., Citation2012. Choice between competitive pairs of frequency models for use in hydrology: a review and some new results. Hydrological Sciences Journal, 57 (6), 1092–1106.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".