Investigating the research landscape of chlorinated paraffins over the past ten decades
Bibliographic record
Abstract
Chlorinated paraffins (CPs) are classified as emerging persistent organic pollutants (POPs). Due to their associated environmental and health impacts, these groups of chemicals have been a subject of interest among researchers in the past decades. Here we used a scientometric approach to understand the research landscape of CPs using literature published in the Web of Science and Scopus database. RStudio and VOSviewer programs were employed as scientometric tools to analyze the publication trends in global CP-related research from 1916 to 2024. A total of 1,452 articles were published over this period, with a publication/author and co-author/publication ratio of 0.43 and 5.49, respectively. China ranked first in publication output (n = 556, 43.3%), and the highest total citations (n = 12,007), followed by Sweden (n = 90), Canada (n = 77), and Germany (n = 75). Publications from developing countries were limited, with most contributions from Africa originating from Egypt (n = 7), South Africa (n = 5), and Nigeria (n = 3), primarily through international collaborations. The average annual growth rate of 4.3% suggests a significant future article output. This scientometric analysis allowed us to infer global trends in CPs, identify tendencies and gaps, and contribute to future research. Despite having similar toxicity to short-chain chlorinated paraffin (SCCP), long-chain chlorinated paraffin (LCCP) has received less attention. Therefore, future research should prioritize studying LCCP bioaccumulation and toxicity in diverse food webs, focusing on aquatic species vulnerable to CPs and effective toxicological models. Additionally, collaborative research with developing countries should be encouraged to enhance meeting the Stockholm Convention's demand.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.045 | 0.111 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".