Choice between competitive pairs of frequency models for use in hydrology: a review and some new results
Why this work is in the frame
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Bibliographic record
Abstract
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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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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