Emerging contaminants as global environmental hazards. A bibliometric analysis
Bibliographic record
Abstract
This paper presents a bibliometric analysis of peer-reviewed scientific literature on emerging contaminants published from 2000 through 2019. A total of 4968 documents (among research articles and review papers) collected from Scopus database were analyzed using the VOSviewer 1.6.11 software. According to our results, this topic has been capturing researchers’ attention over the years and the latter five years of the analysis timespan corresponds to the period of highest scientific productivity on this subject, when a 70.4% of all analyzed documents were published. United States, China, Spain, Italy and Canada were the top–5 most productive countries in terms of number of published works, while Science of the Total Environment, Chemosphere, Environmental Science and Pollution Research, Environmental Pollution and Water Research stood out as the journals with the highest number of publications, gathering a 31% of papers and 34% of all citations. According to the frequency of author keywords, the main specific research topic assessed by the researchers are the occurrence of pharmaceuticals and personal care products in wastewater and the removal of such pollutants by the application of adsorption and advanced oxidation processes.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.106 | 0.137 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".