Socio-Economic Analysis of Soyabeanutilization in Akure South Local Government Area, Ondo State
Bibliographic record
Abstract
This study was carried out for the purpose of analyzing the socio-economic of soyabean inAkure South Local Government Area of Ondo state with the objective of examining the socio-economic characteristics of the respondents Data were collected from one hundred (100) respondents drawn from five communities using well structured questionnaire. The data were analyzed using frequency distribution, percentages and regression model while gross margin was used to determined the profitability of the utilization operators. The outcome of the study revealed that 76% of the respondents involved in utilization were females, about 82% of the respondents were married and 87% were educated. All 100% of the respondent reported that utilization increase their annual incomes. The gross margin result revealed that N11, 877 accrued to a respondent per month in the study area. The outcome of regression analysis revealed that the level of education, occupation, family size, experience and annual income had positive correlation with quantity of utilization whereas, the negative correlation in the storage, inadequate finance, lack of producing farmers, inadequate enlightenment campaign programme by extension workers were emphasized as the problems confronting the utilization in the area. It is recommended that the government should put in place adequate and efficient credit facility to enhance operational activities, provide regular and continuous enlightenment campaign programme by extension workers to the respondents and assist in the regular provision of adequate storage facilities and power supply for preservation of the products.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".