Effect of Coronavirus on Aquaculture in Oyo state, Nigeria
Bibliographic record
Abstract
Coronavirus 2019 is a global health concern that has left most countries in a state of severe economic meltdown. Scientific research has been down on the virus and its impact on various sectors but that of the Nigerian aquaculture industry has been missing. This paves the way for this research to aim at bridging this gap by looking at the perception of fish farmers on the influence of coronavirus on their activities, the challenges they face during the period of the virus, and the coping strategies adopted to mitigate the impact of the virus. The research used cross sectional survey design with the sample size being 11 fish farmers living in Oyo state, Nigeria. Homogeneous purposive sampling was used and primary data collected through the use of google form. The data collected was analysis using SPSS version 25.0. The result of the analysed data showed that: on socioeconomic characteristics; the majority of the respondent reported that Coronavirus has had an effect on their fishing activity and they were mostly small scale farmers with catfish being the predominate fish farmed. The majority of fish farmers perceived demand decline, high cost of production, fish being more expensive, and reduction of manpower on the farm due to lockdown measures. Reduction in walk-in customers to the farm was revealed as the major challenge posed by the pandemic, while the inability to get technical support as least. On coping strategies adopted, it was revealed that farmers have resorted to the development of their own feed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".