Extreme Temperature Events in Kazakhstan and Their Impacts on Public Health and Energy Demand
Bibliographic record
Abstract
Abstract Extreme temperature events such as heatwaves are becoming increasingly severe and frequent because of climate change, posing significant challenges to public health and energy infrastructure. This study explores the impacts of extreme temperature events leading to heat‐/cold waves on public health and energy consumption in Kazakhstan from 1959 to 2021. The most striking trends in heatwave‐related indices emerge in the western and southwestern regions. Conversely, despite heightened coldwave intensity, a decline is noted in their frequency and number. The impact of heatwaves on various health conditions, notably consistent and statistically significant rises in all‐cause and cardiovascular mortalities, is observed. Shifts in energy demand are also unveiled with a noticeable spike in cooling‐degree days and a reduction in heating‐degree days. The mean total energy consumption stood at 552 kWh across the country with an average annual energy generation of ≈8.76 kWh. To gauge the environmental implications, the mean CO2 emissions are estimated at 464 kg per kWh for both heating and cooling purposes. With climate change set to escalate heatwaves, the need for comprehensive health planning is underscored to mitigate their adverse health impacts. Furthermore, transitioning from fossil fuels to green energy sources is crucial to reduce the environmental footprint.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".