Bibliographic record
Abstract
Prominent anthropogenic sources of pollution within urban areas, such as automobiles, industrial operations, and increased electricity usage, are linked to human activities that risk human health. This study aimed to examine the publication patterns and annual growth rates related to urban pollution in the Scopus and Web of Science (WoS) databases. The comprehensive analysis encompasses productive countries, network connectivity, proactive institutions, and research keywords examined through ScientoPy and VOSviewer. This analysis revealed a fluctuating trend in urban pollution research in both databases from 1990 to 2021. Nonetheless, there was a notable surge in publications on the WoS database after 2008. Within the scope of this study, "Environmental Science and Ecology" has been identified as the most pivotal subject area. This study indicated that scholars from France, Brazil, the United Kingdom, Germany, Canada, the United States, and China collaborated extensively, establishing robust research partnerships. The keyword “Urban pollution” has become the most prevalent, followed by “Pollution” and “Air pollution”. This study is subject to certain limitations, primarily from its reliance on the Scopus and WoS databases, which consequently influenced the data quality. Nevertheless, the study elucidates prevailing trends in urban pollution research, offering guidance to practitioners, prospective researchers, and policymakers in formulating novel concepts and a research agenda conducive to sustainable environmental dimensions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.096 | 0.123 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".