Selection of the Best Fit Probability Distributions for Daily Maximum Temperature Data in Six Australian Capital Cities
Bibliographic record
Abstract
Climate change is the most challenging issue in our time. The study of weather extremes like heat waves, droughts, and flooding is important as they pose a significant threat to life, property, and economy. Many deadly natural hazards such as bushfire over the last century in Australia have been caused by the heat waves, which is directly related to the increase in temperature. This study examines the daily maximum temperature data at Australian capital cities. Statistical and graphical goodness-of-fit (GOF) tests are adopted to identify the best-fit distribution probability distributions for the selection of weather stations. The statistical GOF test reveals that Beta, Gamma (3P), Kumaraswamy, and Pearson 5 (3P) distributions generally provide best-fit to the daily maximum temperature data of the selected stations. Results obtained from the graphical GOF test are compared with those from statistical GOF tests. The findings of this study would be useful for engineers, scientists, and planners to investigate the characteristics of the extreme temperature in Australian capital cities. This will assist in achieving sustainable developments in relation to urban heat island effects and other high temperature related problems.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".