Characterisation of Smallholder Irrigation Schemes in Chirumanzu District, Zimbabwe
Why this work is in the frame
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Bibliographic record
Abstract
The study was conducted in 2011 at Hamamavhaire and Mhende irrigation schemes in Chirumanzu district in Zimbabwe to determine the typology of the farmers using different irrigation technologies. A structured household survey was carried out on a sample of 79 respondents drawn from farmers using the sprinkler (n=32), flood (n=39) and drip (n=8) irrigation systems. The information gathered was analysed and interpreted using descriptive statistics and inferential statistics in the form of the chi-square test and Analysis of Variance (ANOVA). The main findings showed that there are significant differences (P < 0.05) in yield per hectare (for green maize, maize-grain, wheat and sugar-beans) across the three irrigation systems. Farmers using sprinkler irrigation were found to be better-off in terms of livestock ownership and household assets compared to those using drip and flood irrigation. The study recommended that there is need to provide agricultural training to farmers in irrigation schemes to enhance their productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it