Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.138 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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 it