Modeling and predicting trends of heat stress based on climate change phenomenon: A case study in a semi-arid climate
Bibliographic record
Abstract
Background: Climate change is one of the most complex human challenges in the future. One of the consequences of climate change is the exposure of people to heat stress, especially in the outdoor environments. The aim of this study was to model the changes in the trend of exposure to heat stress in outdoor environments in the coming decades in the context of climate change and global warming. Methods: The Wet Bulb Globe Temperature (WBGT) index, Canadian Earth System Model (CanESM2), and the Statistical Down Scaling Model (SDSM) were used in a semi-arid climate. In this study, Arak station was considered as a representative of Iran’s semi-arid climate. In this research, the daily data of the minimum and maximum temperatures, humidity, and WBGT index were used from 2011 to 2099. Results: The minimum and maximum air temperatures in the study station show an increasing trend in three time periods. Also, based on the three studied scenarios, air temperature and WBGT index have an upward and positive trend and relative humidity has a downward and negative trend in the coming decades. Conclusion: In general, increasing the exposure of people to heat stress at the study station in the coming decades and based on the simulations of atmospheric general circulation models (GCMs), will not be unexpected.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".