AN ANALYSIS OF STUDIES ON NON-POINT SOURCES OF EUTROPHICATION DURING 1991-2023: A BIBLIOMETRIC APPROACH
Bibliographic record
Abstract
Eutrophication is the gradual loading of nutrients in aquatic systems, and the non-point sources of pollutants have been a natural havoc in mitigating the effects caused by eutrophication.This study condenses various published works about the non-point sources of pollutants into a single study to present the global growth trend of the studies.A bibliometric analysis of the scientific outputs of the topic from 1991 to 2023 was conducted using the data from the Web of Science database.In this regard, 543 documents have been extracted and analyzed with Vos-viewer software and MS-Excel, which identified the growth of publication, most prolific author, most prolific journals, top funding organizations, co-authorship analysis, co-citation analysis, keywords, and SDGs oriented with them.The analysis found that the research in this area shows constructive growth, with China, the USA, and Canada as the most innovative regions with significant contributions.The Vos-Viewer network analysis displays a need for active collaboration and formal cooperation between authors around the globe.It will help bridge the current "non-point sources of pollution" research gap in every country by providing a systemic assessment of existing studies, research hotspots, and evidence to various stakeholders to shape the targets of SDGs.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.131 | 0.201 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".