The Impact of COVID-19 on the Agricultural System and Food Supply in Fiji
Bibliographic record
Abstract
Pacific Island Countries (Kiribati, Fiji, Samoa, and many others) rely on fisheries and agricultural systems for their livelihood and economic development. However, the COVID-19 scenario has led to vast degradation in the agriculture supply, economy, and food security system, resulting in poverty, an increase of unemployment percentage, and a decrease in the tourism industry. The policies related to COVID-19 restrictions, such as lockdowns, access to markets and social distancing, has caused a high reduction in the income of many households. Food purchasing from vendor markets and supermarkets has decreased rapidly due to its prices. Several individuals cannot afford to buy the food items, leading to lower food supply within and outside the country. In addition, several people have been moving to rural areas due to Unemployment. They have started to perform backyard gardening small-scale farming, which again results in lower production of commercial farmers and loss of food supply to consumers. Not only Fiji, but the whole world is experiencing the same situations, which have led to the Government making innovative actions against this deadly virus to protect the citizens from this pandemic. FNPF withdrawals, farming packages, and other initiatives indulged by the Government of Fiji and other Pacific Countries are being discussed in this review. Countries have examined the effects of the Coronavirus on the agricultural system and food supply chain in Fiji and other Pacific nations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".