An extreme analysis for the 2010 precipitation event at the South of Saskatchewan Prairie
Bibliographic record
Abstract
After a prolonged drought period in the early 2000s, the Canadian prairie experienced a remarkably wet year in 2010. Five stations near the edge of the Saskatchewan boreal forest recorded historically high cumulative precipitation (from April to September). The exceptional wet year causes the public concerns on flood controls and land use management in the region. Using the Canadian National Climate Data Achieve, characteristics of six-month cumulative precipitation sums over Saskatchewan prairie are investigated by the Generalised Extreme Value (GEV) Theory. Based on the unconstrained GEV distribution, the 2010 event is outside the estimated 95% confidence intervals for the five Canadian prairie stations. On the contrary, the exceptional high 2010 cumulative perception sums for the five stations are still bounded by the estimated confidence bounds if the GEV distribution is constrained to the Gumbel distribution (i.e. setting the shape factor of the GEV distribution to be zero). These results demonstrate that the classical extreme analysis is useful for planning unprecedented extreme events in the Canadian Prairie, if the GEV distribution is constrained to the Gumbel distribution with the estimated uncertainty bounds based on the order statistics.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".