Enterprise Mix in Integrated Fish Farming in Ogun State, Nigeria
Bibliographic record
Abstract
The study examined enterprise mix in integrated fish farming in Ogun Satate, Nigeria. Using survey research, a pre-tested structured interview guide was used to elicit information from 216 integrated fish farmers that was purposively selected from twenty two villages in four blocks of Ogun State Agricultural development using sampling frame Descriptive and inferential statistics were used to analyze the data. Results showed that 90.7% of IFF was male. Also, 96.8% of IFF was married. The mean ages of sampled farmers were 46 years (IFF) while the mean fish farming experience was 5 years (IFF).The mean fish production capacity of NIFF was 1,894 fish. Furthermore, 11.5% of IFF integrates fish farming with poultry, 7.2% with piggery, and 15.8% and 1.4% with crops and small ruminants respectively. The chi-square analyses showed that knowledge of fish farming had significant association with respondents sex (x2 = 9.44, df = 2, p < 0.05), marital status (x2 = 23.2, df = 4, p < 0.05), occupation (x2 = 25.5, df = 8, p < 0.05), Bivariate correlation analyses showed significant relationship between farmers knowledge and age (r = 0.20, p < 0.05), fish farming experience (r = 0.17, p < 0.05), level of cosmopoliteness (r = 0.16, p < 0.05), livestock population capacity (r = 0.21, p < 0.05), fish production capacity (r = 0.36, p < 0.05), area of land cultivated (r = 0.55, p < 0.05) and production constraints (r = -0.00, p < 0.05).
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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 | 0.001 | 0.001 |
| 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".