Environmental Assessment of the Impact of Atmospheric Air Pollution with Hydrogen Sulfide on the Health of the Population of Atyrau, Republic of Kazakhstan
Bibliographic record
Abstract
This article analyzes the morbidity of newly identified patients in Atyrau, the Republic of Kazakhstan, also calculates the correlation between the content of hydrogen sulfide in the air and mortality from diseases for a year by decade from July 2021 to June 2022.According to the analysis of morbidity over ten years, it was determined that the maximum number was detected for respiratory diseases, in second place diseases of the circulatory systems, the number of which exceeds 16,000 and 2,000 patients per 100,000 people, respectively.The calculation of the correlation dependence showed a direct positive relationship between the content of hydrogen sulfide in the air and the mortality of people from diseases of the respiratory and circulatory systems (the correlation coefficient of which is 0.99 and 0.5 respectively), and there is a negative dependence on the mortality of neoplasm diseases (the correlation coefficient is (-0.09)).This study has limitations on data, since the incidence data were given only for decades, whereas the correlation dependence would be more accurate with more data.In the future, it's planned to continue this study and include calculations of the relationship between the number of patients and the content of hydrogen sulfide for each month.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".