Mapping Taihu Basin research: a bibliometric analysis
Bibliographic record
Abstract
The Chinese government has made a strong effort to solve pollution problems in the Taihu Basin, and scholars have also paid close attention to these issues. Based on 2094 valid studies on the Taihu Basin obtained from the Web of Science (WoS) core database from 1998 to 2019, this study conducted a systematic review of research and development trends using bibliometric methods. The results reveal that academic achievement has increased rapidly in recent years, especially after a severe blue-green algae bloom event in Taihu Lake in 2007. Taihu Basin related studies are becoming more interdisciplinary in nature with an integration of knowledge. The Chinese Academies of Sciences and universities in eastern China play key roles in academic output. Although hotspots of environmental topics vary at different time periods, the eutrophication issue at Taihu Lake has long been a focus. Eutrophication studies on Taihu Lake have evolved from “general causes of eutrophication” to “more detailed causes of eutrophication” to “long-term monitoring and management of water quality” to “risk assessment”. Future trends in Taihu Basin research should continue to emphasize risk assessment and long-term management. This bibliometric review presents a comprehensive analysis of Taihu Lake related research, which can provide important insights into the potential direction for future studies.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.151 | 0.212 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".