Climate warming effects on temperature structure in lentic waters: A bibliometric analysis from the recent 20 years
Bibliographic record
Abstract
Global climate warming and intensified summer heatwaves have exacerbated thermal stratification in inland lakes and reservoirs, leading to increased issues of deep-water hypoxia and harmful algae blooms. This study aims to systematically review the developments, barriers, and future directions of thermal phenology in freshwaters under warming conditions through a visualized meta -analysis. According to the Web of Science Core Collection database, we retrieved 3262 articles published between January 1, 2000, and December 31, 2023, using an advanced search query that included terms related to global warming, temperature stratification, and freshwater bodies. The data was then analyzed via bibliometric visualization tools to create comprehensive visual maps, highlighting research hotspots and development trends. Key findings include a significant upward trend on this topic, in the annual number of published articles post-2015, in which China and the USA are leading in the publication output. Keyword co-occurrence analysis identified climate change, global warming, and temperature as central themes, with specific environmental issues linking to lake eutrophication and runoff being prominent as well. The study also delves into the collaboration networks among researchers, institutions, and countries, revealing strong international partnerships primarily between China, the USA, and European nations. Based on the analysis, we recommend future research should focus on integrating machine learning and advanced modeling techniques to better predict and mitigate the impacts of climate warming on thermal dynamics of inland waters. By upscaling the research from traditionally local (or regional) to global perspective, our work is vital, not just for science, but also for management of the aquatic systems under rapidly changing climate conditions.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.147 | 0.176 |
| 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) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".