Sustainable development goals (SDGs), climate change, and the development of aquaculture and fisheries industries
Bibliographic record
Abstract
Aquaculture, along with commercial fishing, provides fundamental support for maintaining worldwide food security and economic progress and nutritional wellness. These sectors are confronting rising challenges and risks, which include climate change along with overfishing and non-sustainable practices. Hence, this review investigates the immediate effects of climate change on fisheries and aquaculture institutions through varying temperature conditions along with shifted precipitation cycles, coupled with ocean acidification and extreme weather occurrences. We evaluate how fish distributions respond, and physiological processes change, alongside ecosystem dynamics because of these alterations. The review also explores the sustainable development goals connectivity of aquaculture and fisheries to identify mechanisms that combat poverty and hunger alleviation and resolve environmental problems. This review further corroborates worldwide adaptation methods by analyzing combinations of species dispersal along with selective breeding and habitat recovery and innovative fish farming systems. However, many studies provided evidence on success rates of these methods and require community participation coupled with technological development along with relevant local and regional policy support. Different geographical areas show evidence of effective adaptation strategies through studies that prove the success of integrated traditional approaches and community involvement in decision-making frameworks. Finally, this review demonstrates the importance of developing a comprehensive solution to enhance aquaculture and fisheries resilience against climate change, as food security and socio-economic stability depend on the adoption of sustainable practices as their core operating principle.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".