Assessing Climate Variability using Extreme Rainfall and Temperature Indices
Bibliographic record
Abstract
Future climate change is generally believed to lead to an increase in climate variability and in the frequency and intensity of extreme events. Extreme climate events such as floods and dry spells have significant impacts on society. As noted by the Bureau of Meteorology, Canada, to examine whether such extremes have changed over time a variety of extreme climate indices can be defined, such as the number of days per year which exceed, or fail to exceed, fixed thresholds. However, since people tend to adapt to their local climate, a threshold considered extreme in one part of Australia could be considered quite normal in another. To overcome this problem, thresholds based on percentile values have been defined by the Bureau of Meteorology, Canada. In this present study, three indices of extreme rainfall were examined: the number of events above an extreme threshold (extreme frequency); the average intensity of rainfall from extreme events (extreme intensity); and the proportion of total rainfall from extreme events (extreme percent). The same exercise was repeated using daily temperature values over the same time period. The aim was to assess whether the island was already experiencing variability in its climate pattern, as such an information would be very useful for decision making. Keywords: Climate change, Rainfall Variability, Extreme Rainfall Indices
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".