Bibliometric Analysis of the Effects of Aquaculture on Mangrove Forests
Bibliographic record
Abstract
Mangroves are coastal ecosystems that stand out for their biodiversity, carbon sequestration, and natural flood defences. These ecosystems face significant threats from human activities, particularly aquaculture. This research uses bibliometric techniques such as the evolution of scientific production, bibliographic coupling by country, and cooccurrence of keywords to identify trends, collaboration networks, and emerging research areas using the Scopus database, chosen for its broad coverage of high-quality academic journals and peer review. This analysis describes the evolution and trends in mangrove studies, covering environmental, social, and legal issues. The methodological process was divided into three stages: design and data collection strategy, filtering and validation of the literature, and quantitative analysis to identify trends and thematic evolutions. A total of 993 documents from 39 countries have been reviewed, with the main contributions coming from China, the United States, and Indonesia. The study identified four priority areas for the development of research and future trends on the following topics: a) evaluation of heavy metal pollution, b) blue carbon and its impact on climate change mitigation, c) conservation and protection strategies, d) the use of remote sensors and machine learning for monitoring mangrove loss. These approaches are crucial for conserving mangroves, improving understanding and response capacity to climate change, and contributing to Sustainable Development Goals, considering the socioeconomic value of these ecosystems.
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How this classification was reachedexpand
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 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".