Spatiotemporal variations of aridity in Iran using high‐resolution gridded data
Bibliographic record
Abstract
Aridity is a permanent feature of climate based on long‐term climatic conditions over a region. Climatic indices are reliable tools to explore climate type, and climatologists have proposed various indices to classify climate and investigate the aridity or humidity in any region. In this study, we examined spatiotemporal variations of aridity in Iran during the last six decades from 1954 to 2013, using the de Martonne aridity index (IDM), which is calculated based on precipitation and temperature. Data used in this study were extracted from the Global Precipitation Climatology Centre and the University of Delaware gridded data sets, respectively. Both data sets have global high‐resolution (0.5° × 0.5°) coverage, and temporally span more than a century of data (from 1901). According to the data obtained from these data sets, more than 80% of Iran has an arid and semi‐arid climate (annually), although the spatial pattern of IDM varies throughout the year. Using the Mann–Kendall test showed a negative significant trend in IDM in 20% of Iran's total area in spring, and less than 7% in the other seasons of the year. Overall, it can be concluded that there were no significant trends in aridity for most parts of Iran during the last six decades.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".