Assessing the Carbon Dioxide from Transportation and Health Outcomes Within Duhok City
Bibliographic record
Abstract
This study examines the correlation between the expansion of urban areas, the release of transportation-related pollutants, and the effects on public health in Duhok City. It particularly concentrates on the influence of CO2 emissions resulting from an increase in transportation activities. The study area, Duhok City, situated at the intersection of Iraq, Turkey, and Syria, is currently undergoing swift urbanization and a consequent increase in the utilization of private vehicles as a result of poor public transit networks. This phenomenon has led to substantial carbon dioxide emissions, which in turn contribute to air pollution and have negative health impacts on the population. The methodology includes a combination of quantitative and qualitative data collection methods, such as measuring air quality, counting traffic volume, and conducting an interview with health specialist and questionnaires to assess health impacts. The research conducts a thorough analysis of current literature and collects empirical data to investigate the connection between emissions from transportation and public health. Its objective is to discover patterns of exposure and the resulting health effects. And The results suggest that the increase in vehicle emissions is a significant factor in the worsening air quality in Duhok, which worsens health problems such as respiratory and cardiovascular diseases. The study enhances our comprehension of the environmental and health obstacles encountered by fast urbanizing cities in conflict-prone areas. It proposes ways for alleviation, with a particular emphasis on improving public transportation systems and lowering reliance on private vehicles as a means to control CO2 emissions.
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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 | 0.001 | 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) | 0.001 | 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".