Aquaculture Production and Value Chains in the COVID-19 Pandemic
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to summarize the impacts of the coronavirus disease 2019 (COVID-19) pandemic on aquaculture input supply, production, distribution, and consumption. RECENT FINDINGS: The COVID-19 pandemic-related lockdowns, social distancing, supply chain disruptions, and transport restrictions affect seafood production, distribution, marketing, and consumption. Recommendations are suggested to overcome these challenges. The COVID-19 has led to disruption of aquaculture practices worldwide. The pandemic has adversely affected the aquaculture input supply of fish stocking and feeding, which, in turn, has impacted aquaculture production. Moreover, the COVID-19 crisis has had adverse effects on value addition to aquaculture products, through the restrictions of seafood marketing and exporting. Aquatic food production is vulnerable to the effects of COVID-19 outbreak; hence, adaptation strategies must be developed to cope with the challenges. There is an urgent need for collaboration among key stakeholders to rebuild the supply chain of inputs and fish marketing for sustainable aquaculture practices. International agencies, donors, government and non-governmental organizations, researchers, and policymakers need to develop policies to support aquaculture production and supply chains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".