Historical Changes in Australian temperature extremes as inferred from extreme value distribution analysis
Bibliographic record
Abstract
[1] This study develops a generalized extreme value (GEV) distribution analysis approach, namely, a GEV tree approach that allows for both stationary and nonstationary cases. This approach is applied to a century-long homogenized daily temperature data set for Australia to assess changes in temperature extremes from 1910 to 2010. Changes in 20 year return values are estimated from the most suitable GEV distribution chosen from a GEV tree. Twenty year return values of extreme low minimum temperature are found to have warmed strongly over the century in most parts of the continent. There is also a tendency toward warming of extreme high maximum temperatures, but it is weaker than that for minimum temperatures, with the majority of stations not showing significant trends. The observed changes in extreme temperatures are broadly consistent with observed changes in mean temperatures and in the frequency of temperatures above the ninetieth and below the tenth percentile (i.e., extreme indices). The GEV tree analysis provides insight into behavior of extremes with re-occurrence times of several years to decades that are of importance to engineering design/applications, while extreme indices represent moderately extreme events with re-occurrence times of a year or shorter.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".