Analysis of the number of bibliographic publications on the presence of microplastics in sediments in America with the Scopus search tool
Bibliographic record
Abstract
Objective: the analysis was to examine the scientific activity of the American continent in detail during a period of ten years (from 2014 to 2024). Design/Methodology/Approach: using a quantitative methodology, with articles that refer to the presence of microplastics (MPs) in sediments available in the Scopus database. Results: 2,050 articles were collected, of which 123 (6%) corresponded to publications of MPs in sediments, dominating Brazil, the United States, Canada and Mexico, along with the main cited authors Alexander Turra, Patricia L. Corcoran, A.D. Vethaak. The methodology used was diverse: separation by density of microplastics, using an oil extraction protocol and FTIR spectroscopy, among others. Study limitations/Implications: there is no specific methodology for the main steps in the determination of MP, considering the increase in detection in distinct environmental sediments. Findings/Conclusions: the production of scientific articles on MP in sediments has increased over the last 10 years, due to the subsequent detection of the presence of these contaminants in sediments.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.144 | 0.117 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".