An Analysis of Highest Diurnal Precipitations Changes and Their Related to Annual Precipitations in Iran
Bibliographic record
Abstract
In this paper, in order to study of the highest daily precipitations of daily precipitations data in 46 synoptic and climatologic stations that almost covered all part of Iran have been got from the national weather organization and have been used after the extraction of necessary data for a 30-year period from 1976 to 2005. In concerning of data analysis, necessary statistical analyses like data homogeneity test, normality test, correlation test, simple and multiple regressions, variance analysis, Friedman's non parametric test and classification cluster analysis have been used. The results showed that the average of the highest precipitations in all the studied stations during the statistical period of the research, except in two stations of Boushahr and Khoy, don't have any significant difference. Investigation of the changes of the highest annual precipitations in the statistical period in each of these stations showed that these changes are significant in Sanandaj station and in the other stations no significant change is observed. The results also proved that there is a positive and significant relation between the highest daily precipitation and annual precipitation in all the stations which means increase in the annual precipitation the highest daily precipitation of stations growth. Finally climatic classification of the stations displays 5 layers in terms of the proportion from highest daily rain to annual rain in the country.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".