Aquaculture Production and Value Chains in the COVID-19 Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it