Factors Affecting the Rice Yield During the Rainy Season Among Farmers in Southeastern Cambodia
Why this work is in the frame
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Bibliographic record
Abstract
A research study utilized the Cobb-Douglas production function to examine the elements influencing paddy production during the wet season in three rural provinces of Cambodia. This analysis was based on data gathered from a survey of farmers’ households conducted in 2022. The study discovered that the use of fertilizers and herbicides, the size of the family, and income from off-farm sources significantly impacted the output of wet-season paddy. A one percent increase in the use of fertilizer, herbicide, and family size resulted in an increase in rice output by 0.06 percent, 0.04 percent, and 0.05 percent respectively. Furthermore, a one percent increase in the age of the household head, hired labor, and off-farm income led to an increase in rice yield by 0.08 percent, 0.11 percent, and 0.05 percent respectively. The use of seeds, pesticides, household labor, and the education level of the household heads were found to enhance rice yields in southeastern Cambodia. However, these production relationships varied significantly across different regions. The study concluded that higher yields during the rainy season improved the effectiveness of paddy production, primarily due to the increased responsiveness to fertilizer application.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 it