Global Trends in Caffeine-based Lifestyles: A CiteSpace Exploration of Potential Environmental Sustainability Impacts
Bibliographic record
Abstract
Caffeine-based lifestyles have become increasingly common, reflecting its widespread consumption as a psychoactive stimulant found naturally in coffee, tea and cocoa yet synthetically in personal care products (PCPs) and pharmaceuticals. This trend has raised concerns about its potential environmental consequences as it can bioaccumulate in various species, including aquatic organisms and terrestrial insects, prompting continuous research in this field since decades ago. However, a comprehensive bibliometric and scientometric analysis in this research field appears lacking. Thus, the primary goal of this study is to analyse the scientific literature concerning the impact of caffeine-based lifestyles on the environment. A CiteSpace analysis was applied in this study to determine various aspects of research literature, including the identification of productive authors, institutions, journals, regional distribution and emerging issues in the field. The study yielded 869 relevant publications from the Web of Science Core Collection (WOSCC) database. The results revealed that the United States, China, Canada, Brazil, and Spain were the top five countries out of 3345 writers from 108 countries active in this research area on the top of keywords such as PCPs, pharmaceutical and wastewater. The present study provides the existing body of knowledge on this topic by sharing a visual knowledge map, which highlights the trend, offering a valuable perspective, and opportunity for researchers.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.053 | 0.074 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".