Air Pollution due to Desertification and Diseases Caused by It
Bibliographic record
Abstract
Pollution is one of the most dangerous threats to humans and even animals.Desertification and climate change are the most important environmental challenges in arid regions, which affects the health of people all over the world such as air pollution.Air pollution has become a global problem today, which threatens the health of humans around the world.Air pollution although is harmful for all people of all ages but a wide range of people, including children, the elderly, pregnant women and patients are more vulnerable than others.Most of the pollution caused by air pollution is related to the respiratory system and lungs, the immune system, the heart, and the vision system.In the study of air pollutants and dust particles, dust and greasers are significant parts.In this research, we tried to explain about these materials a little and how they are produced, their mode of action, the diseases cause by them and the ways to prevent these harmful pollutants.Also we studied the climatic parameters (precipitation, temperature, evapotranspiration and wind) to determine dry month of area and the effect of it in Khuzestan province (IRAN) that is affected by dust since 2003.Based on the results of the most important climatic factors that influence desertification and air pollution of the Khuzestan the month of April to mid-October, the region was dry, so that, June, July and August seems to be its greatest extent.The prevalence of diseases caused by dust and air pollution is the most in these months.This study was prepared with the help of a group of Iranian medical professors in a descriptive method.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".