A Metaverse Platform for Air Pollution Analysis in Supporting Smart and Sustainable City Development
Bibliographic record
Abstract
In today's data-centric world, analyzing vast volumes of diverse and complex information has paved the way for uncovering valuable insights. These extensive datasets are often known as big data. Big data find application in various fields such as healthcare, gaming, financial markets, and business intelligence. Additionally, analyzing big data can contribute to enhancing environmental sustainability, as well as city planning and development. On the one hand, air pollution in urban areas is frequently identified as a major factor negatively affecting human health, with vehicle emissions being a significant contributor to poor air quality. On the other hand, increased greenspace and vegetation positively contribute to better air quality. In this paper, we present a data science and advanced analytics solution—specifically, a metaverse platform—to examine the relationship between urban factors and air quality. Our solution leverages data mining and visualization techniques in a metaverse platform to extract meaningful insights. Moreover, we analyze real traffic data from a mid-size Canadian city to guide our study. The findings from this data science research can inform practical strategies—such as promoting green infrastructure and implementing zoning policies—towards building and development of smart and sustainable cities.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".