Bibliographic record
Abstract
Abstract This review outlines nationwide methods for point rainfall frequency estimation currently in use in nine different countries: Canada, Sweden, France, Germany, the United States, South Africa, New Zealand, Australia and the United Kingdom. For the United Kingdom, the Flood Studies Report method from 1975 is described as well as the current Flood Estimation Handbook method. The focus is on return periods relevant to reservoir design, in the region of 100–10 000 years. There is considerable difficulty in estimating long return period rainfalls from short data records and there is no obviously ‘best’ way of doing it. Each country's method is different, but most use some form of regionalisation to transfer information from surrounding sites to the target point. Several of the methods are variations of a regionalisation method that combines a local estimate of an index variable (typically the mean or the median annual maximum rainfall) with a regionally derived growth curve to obtain a design rainfall estimate. Three of the methods use regions centred on the site of interest, rather than fixed‐boundary regions. Different statistical distributions and fitting methods are used, with the Generalised Extreme Value distribution being the most common.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".