The Role of Digital Technologies in Supporting Climate Change Adaptation in Fisheries and Aquaculture
Bibliographic record
Abstract
The Indeed, future climate change needs calls for action since its effects are seen. The impacts of climate change seen over the years may include rising mean temperatures, sea level rise, explosive intensification of cyclones, irregular precipitation patterns, soil and beach erosion and the exponential increase in extreme weather conditions. One of the main focuses that has retained international attention is unequivocally the food crisis. Fisheries and aquaculture have long been the source of food for many nations across the world where the impacts of climate change compelled resilient measures. Evidence has been seen in regions such as Bangladesh and Africa where climate change has highly affected fisheries and aquaculture. One of the exacerbations of climate change would be the rising temperatures. It has also been observed that temperature has a direct impact on the physiological development of fish and shellfish. This report demonstrated the paramount of studies available to address climate change and the role of digital technology as an adaptation strategy. Results were further classified into 4 distinct areas such as Internet of Things (IoT)-Artificial Intelligence (AI)-Blockchain, Genetics, Geographical Information Systems and Digital technology-business models. Research permitted the discovery of several tools and technologies adapted to increase production amidst coercive climatic conditions. It has been noted that this sector was very keen to adopt new digital technologies to improve production. It followed that genetically strengthened fish species could adapt to areas where the impacts of climate change are very harsh and survival rates for species are very low. Research has been done on innovative digital technologies, but are seemingly under review, where fish cages or aqua pods could provide additional support for fish production. Digital technologies enhance aquaculture operations by providing real-time water quality and fish health monitoring, increasing efficiency, enhancing decision-making, detecting diseases early and promoting sustainability. They streamline processes, reduce labour costs, and optimize resource use, ultimately leading to better fish health and reduced antibiotic use. Nevertheless, challenges in aquaculture adaptation strategies, include data management, cybersecurity, cost, accessibility, skills training, and regulatory framework adaptation. These challenges can compromise data and operations, limit access to digital technologies, and require skill training for aquaculture operators. Addressing these challenges is crucial for realizing their benefits. It has been concluded through this study that aquaculture and fisheries industries have very promising futures. The digital technologies involved in improving production are no less to evolve further. The use of machinery and tools is next to step into another hi-tech age where industry 5.0 is cited as the coming future. Connecting the transregional, national and international innovations are key successes to the fisheries and aquaculture sector.
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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 | 0.000 | 0.000 |
| 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".