The nexus of IoT and aquaculture: A bibliometric analysis
Bibliographic record
Abstract
The Internet of Things (IoT) in aquaculture presents significant opportunities for improving the sector's productivity, sustainability, and resilience. This study aims to achieve two primary goals: to deliver an extensive overview of IoT applications in aquaculture and to pinpoint emerging trends and research gaps, thereby directing future academic endeavors in the aquaculture field. Through bibliometric analysis, which involved keyword co-occurrence and article co-citation network analyses, we investigated 428 publications from 2012 to 2024 retrieved from Scopus. The review indicates a significant rise in investigative efforts, especially in recent years, highlighting the sector's increasing focus on the role of IoT in tackling the distinct challenges aquaculture faces, including water quality monitoring, disease prevention, and resource efficiency. Prominent themes recognized encompass advanced aquaculture systems, water quality and health monitoring, and sophisticated forecasting tools. This investigation enhances the existing knowledge base by emphasizing key themes, significant studies, and essential technological advancements in IoT-enabled aquaculture, providing one of the initial bibliometric assessments in this swiftly developing field. Future research should focus on enhancing interoperability among IoT devices, improving data security and privacy, integrating artificial intelligence for predictive analytics, and expanding IoT applications to support small-scale and resource-constrained aquaculture operations.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.183 | 0.256 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".