Iran’s Changing Climate over the Past 30 Years
Bibliographic record
Abstract
In this study, monthly meteorological parameters of temperature, precipitation, and wind speed recorded at 104 synoptic weather stations scattered across Iran were analyzed to construct annual time series of the meteorological data for a 30-year period (1988–2017) using the Mann–Kendall test and the Sen’s slope estimator. Based on the de Martonne aridity index ( $$I_\mathrm{DM}$$ ) classification, Iran’s climate is mostly categorized as dry and semi-dry. The results indicated annual temperature significantly increased at about 61% of stations and annual precipitation significantly decreased at 21% of stations (at the 95% and 99% significance level). The decline in precipitation occurred in mostly the dry (27%) and semi-dry (21%) regions of Iran. Approximately half of the stations experienced significant increases in wind speed with arid areas experiencing broadest increase. Prediction of future impacts of climate change is needed for this region to mitigate negative effects.
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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) | 0.001 | 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".