Global Warming in Pakistan and Its Impact on Public Health as Viewed Through a Health Equity Lens
Bibliographic record
Abstract
Pakistan is extremely vulnerable to the negative impacts of climate change. The recent monsoon season caused widespread, deadly flooding, affecting 15% of the total population when extreme heat waves were followed by the worst rains and floods in the country's history. But Pakistan was not the cause of its own misfortune. The atmospheric buildup of carbon dioxide (CO2) is the greatest contributor to climate change. If we look at the increase of carbon dioxide in the atmosphere, we find that Pakistan is, like all developing nations, essentially a non-contributor of the problem, contributing considerably less than 1% of global greenhouse gas emissions. Moreover, although significant factors exacerbating the effects of climate change in Pakistan include an inadequate sewage system, air pollution from industrial waste, and deforestation, the country could not afford to proactively fix these, nor prepare for flooding and heavy rains. It lacks the funding for climate resilience efforts. As a result, Pakistan is suffering from a high prevalence of poor health outcomes. Children, the elderly, women, and the homeless, especially those living with poverty and disease, are at a high risk of morbidity and mortality. Since mitigating the devastating effects of climate change will continue to be an ongoing challenge for Pakistan, it urgently needs financial investment so that it can build climate-resilient infrastructures and institute mechanisms to deal with global warming's worst effects. Industrialized nations are responsible for global warming, and they must take responsibility for fighting global warming by helping developing countries cultivate greater public health emergency preparedness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".