Comparing carbon dioxide levels within urban transport microenvironments during rush hour and non-rush traffic
Bibliographic record
Abstract
Introduction: Commuters spend countless hours within tightly confined spaces with limited ventilation that may be filled with many contaminants. By analyzing if there is a significant difference between levels of carbon dioxide between rush and non-rush hour conditions, it can be determined if some commuters are subjected to poorer levels of air quality during certain times of the day. Objectives: The primary objective of this study is to understand whether there are significant ventilation deficiencies during rush compared to non-rush hour times in urban transport microenvironments. Methods: Analysis of urban transport microenvironments was done using the TSI brand QTrak Indoor Air Quality (IAQ) Monitor to gather data on carbon dioxide at 1-minute intervals on the 99 B-line express bus that runs between Broadway and Commercial Skytrain Station and the Broadway and Cambie Street Skytrain Station. Results: A one tailed T-test was done on the NCSS 9 statistical software to compare if rush hour urban microenvironments had significantly higher concentrations of carbon dioxide than when compared to non-rush hour. Statistical analysis determined that since the P-value was well above the alpha level of 0.05 (i.e. P<0.05), it gives reason to accept the null hypothesis, which states that rush hour concentrations were not higher than non-rush hour. Conclusion: Statistical analysis determined that the overall concentrations of carbon dioxide during rush hour were not significantly higher than non-rush hour times. This result may have been attributed to conditions and factors during data collection that could not be controlled by the researcher. Due to the length of the route, exposure times were found to be within time-weighted averages as set out by the American Conference of Industrial Hygienists (ACGIH), though it was still not within the recommended limit of 1000 ppm as set out by the American Society of Heating, Refrigeration and Air-Conditioning (ASHRAE).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".