Agricultural advisory service (AAS) in responding pandemic: A global review and reflection
Bibliographic record
Abstract
One of the significant aspects of human life adversely affected by the novel coronavirus pandemic is agriculture. Various preventive measures have been put in place by the governments of various countries to curb the spread of the disease. Despite the positive impact of these precautionary measures, in its execution, the production of food and other agricultural products has been affected.This situation, therefore, has called for the mobilization of all physical and institutional resources in the agricultural sector to avert the impending famine, which will be the result of a continued reduction in the production of food.The importance of the agricultural advisory service in helping the agricultural sector wade through difficult times has been tremendous over the years. To enable the advisory service to continue this beneficial role during this pandemic, the need for digital agricultural advisory services to be intensified and continued is imminent.A detailed review of the literature bordering on the topic was carried out to review the application and effectiveness of the digital agricultural advisory services during the pandemic. It was discovered that the application of digital agricultural advisory services predates the pandemic era. The use of the digitalized form of advisory services for the agriculture sector has yielded great results for the sector before the pandemic. The continued adoption during the pandemic era will also boost food and other essential agricultural products during this pandemic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 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".