Evaluation of Seasonal Changes in Temperature and Precipitation for Iran Five Provincial Centres during 1960-2017
Bibliographic record
Abstract
Climate change is one of the key challenges of our era and it is a threat to sustainable development. Global warming has many meteorological consequences including rising air temperatures across the world. Undoubtedly, human activity has been one of the key factors to global warming followed by increased greenhouse gas emissions which will exacerbate changes in the Earth’s climate variables. So, any research work related to the climate around the world including Iran due to climate change may cause to better understand the cause and effect and make a better adaptation. This study investigates the regional warming in five meteorological stations in central provinces of Iran, based on seasonal changes in precipitation and temperatures over the period of 1960-2017 (study period). The seasonal drought severity based on Palmer index during 1960-2005 was used to monitor the drought intensity in the study areas which are in drought risk situation. The classification of drought severity using Palmer index shows the severe drought intensity in Arak, Qom, Semnan, Tehran and Isfahan respectively in all four seasons, especially during fall and summer. The slight changes in the coefficients of seasonal maximum, minimum and mean temperatures have been resulted. According to these results, the highest maximum (minimum) temperature rise has been calculated for Qom (Tehran) station during spring and winter (fall) seasons ~0.44°C (~0.67°C) in a decade during 1960-2017. However, the highest decrease in precipitation over Arak station has been calculated ~13.8 mm in a decade in winter during study period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".