Characteristics and development trends of ecohydrology in lakes and reservoirs: Insights from bibliometrics
Bibliographic record
Abstract
Abstract Ecohydrology, an interdisciplinary subject connecting ecology and hydrology, has developed rapidly in recent years. Because lakes and reservoirs are responsible for the drinking water supply of billions of people and water issues are becoming increasingly severe, the importance of these water bodies is self‐evident. Lake (reservoir) ecohydrology has thus attracted considerable attention. This study aimed to analyse the characteristics and development trends of ecohydrology using a bibliometric analysis based on the Science Citation Index database. A total of 21,753 papers from 1900 to 2017 on lake (reservoir) ecohydrology were published in 2,323 journals, and the large majority of them were published in the past three decades. Most research has been concentrated in Europe (40.0%) and North America (31.0%). Among these studies, a few key lakes, for example, Lake Taihu, Lake Erie, Lake Michigan, and Lake Ontario, have been analysed in detail by numerous researchers. The results of a word frequency analysis show that the topics related to ecohydrology have transformed from the microcosmic perspective to the macroscopic perspective, and major topics include eutrophication , global change , models , and ecosystem management . The results of a principal component analysis show that the scope of lake (reservoir) ecohydrology research in Europe and North America has stagnated in recent years, whereas in China, ecohydrology research has developed rapidly over the same period. The development of ecohydrology research around the world is not even, and we need to push for more research on major lakes that are outside of Europe, the United States, and China.
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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 | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".