Distribution choice for the assessment of design rainfall for the city of London (Ontario, Canada) under climate change
Bibliographic record
Abstract
With the effect of global climate change the rainfall intensity is changing, and in many places it is drastically increasing. The use of intensity–duration–frequency (IDF) curves based on historic rainfall data might, therefore, underestimate the risk associated with the design and assessment of drainage systems. The theoretical probability distribution function used in the establishment of IDF curves based on historical observations might need to be different for the future conditions. The Gumbel (EV1) distribution is the currently recommended distribution for use in Canada and the EV1 and the Log-Pearson type 3 (LP3) are routinely used in the US. This study investigates potential utility of the generalized extreme value (GEV) distribution for use in climate change impact studies by the City of London that is located in the Upper Thames River Basin. All results point that GEV seems to be the best choice for the use with the Upper Thames River Basin data. We would like to use results of this study and open the discussion on the choice of most appropriate distribution for the development of IDF curves under changing climate conditions in 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".