Bibliographic record
Abstract
Almost 1 out of every 9 people on the planet Earth go to bed without food almost on a daily basis. Nigeria ranks 20th on the Global Hunger Index, with about 65% of her population confronted with food insecurity. The country has an estimated 84 million hectares of arable land of which only 40% is cultivated. There is huge potential in forestry, animal husbandry, fisheries, food and cash crops. How to harness these potentials into prosperity and food security still remains a challenge. The paper is set out to investigate the challenges militating against sustainable agricultural practices in Nigeria and suggest ways as to how these challenges can be surmounted. The goal of this paper is how to meet the food needs of this nation without compromising the ability of future generations to meet their own needs. The study found that despite various attempts at addressing food shortage in Nigeria, the nation still remains insecure as far as food is concerned and that Nigeria is yet to attain sustainable agricultural development despite her robust agricultural laws. The paper identified agriculture as an indispensable requirement for life sustenance and the best way to end poverty. The paper concluded that agriculture, which is a major platform for national development as well as one of the major drivers of the economy of any nation, remains a very important engine of economic development. A legal framework for sustainable agricultural practice that is carefully designed and implemented with the necessary political will was postulated.
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 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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".