Reviewing the Impacts of COVID-19 Pandemic on the Kenyan Aquaculture Sector and Future Adaptive Strategies
Bibliographic record
Abstract
For many Kenyans, the aquaculture business provides a vital source of food and work. However, information on Kenya’s aquaculture sector’s resilience in the face of emerging global shocks such as the COVID-19 pandemic requires additional examination. Prior to the epidemic, Kenya’s aquaculture industry had grown from a tiny participant to a critical component of the country’s fish food system, with fish and fisheries products becoming the most extensively traded food commodity in Kenyan market places. However, as indicated in the review, the aquaculture value chain has not been scrutinised since the onset of COVID-19. Lockdowns enacted during the pandemic had a significant influence on access to aquaculture inputs, fish commerce, and the socio-economic livelihoods of stakeholders and players in Kenya’s aquaculture value chain. Thus, initial and long-term adaptive strategies, particularly those implemented by governments, could help to the development of COVID-19 specific and generic resilience to numerous shocks and stressors among stakeholders and players involved in the country’s aquaculture industry. Some of the measures include a government incentive package to help the fisheries and aquaculture sectors recover, improve farming operations, and gain market trust, as well as the adoption of new methods to reduce labor intensity, such as intelligent sensors, camera systems, and automated or remotely controlled monitoring/feeding strategies. Such strategies and policies can protect the sector from future shocks triggered by pandemics and other unforeseen circumstances.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".