Bibliographic record
Abstract
The fastest-growing industry in food production is aquaculture, which is essential to supplying the world's protein needs. This review examines aquaculture's sustainable practices and technological developments. The industry witnessed a transition from conventional techniques to more sustainable and effective systems as worries about resource depletion and environmental degradation grew. Technologies like Biofloc, Integrated Multi-Trophic Aquaculture, and Recirculating Aquaculture Systems (RAS) decreased waste discharge and increased water use efficiency. Aquaculture operations' ecological footprint has decreased as a result of feed development innovations, especially those involving plant-based and alternative protein sources. Furthermore, improvements in health management, such as the use of vaccines, probiotics, and improved diagnostic equipment, have significantly decreased the incidence of disease outbreaks and the use of antibiotics. Additionally, selective breeding and genetic advancement for disease resistance and quicker growth were emphasized. Government laws and international collaboration supported sustainable development, while certification programs like the Aquaculture Stewardship Council (ASC) and GlobalG.A.P. encouraged ethical behaviour. Despite these developments, there were still issues with small-scale farmers' adoption of new technologies, high operating costs, and uneven regional enforcement of policies. In order to guarantee aquaculture's long-term sustainability, this review emphasizes the necessity of ongoing innovation, capacity building, and policy support.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".