Investigation Regarding the Correlation between Particulate Matter 2.5 Air Pollution and Mortality Rates due to Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
In 2015, 3.2 million people died due to Chronic Obstructive Pulmonary Disease (COPD), worldwide. In fact, survival rates for those living with severe COPD are lower than for those with cancer. The one known contributor to this disease is air pollution, and with its rising levels every year, it is necessary to determine the exact correlation between air pollution and COPD. Data was gathered for a selection of 20 countries from the World Bank Database and Health Data Database. This data was graphed and analyzed using the Pearson correlation coefficient, which is a statistical test that measures the relationship between 2 variables. When calculated, the Pearson correlation coefficient was 0.756, determining that there is a significant relationship between air pollution and COPD. Through the investigation, it is concluded that there is a positive correlation between PM2.5 air pollution and mortality rate due to COPD. PM2.5 is a component of air pollution defined as the amount of atmospheric particulate matter with a diameter less than 2.5 micrometers. Due to its small physical nature, PM2.5 can easily infiltrate the lungs, causing infections in the respiratory organs. They can reach the bronchi and even the alveoli, causing inflammation which ultimately results in COPD and premature deaths. Therefore, this research will aim to investigate the relationship between PM2.5 air pollution and COPD, allowing for a better understanding of these variables.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".