Pattern design for capacity building of promoters in climate-smart agriculture environment
Bibliographic record
Abstract
This study aimed to investigate the impact of agricultural environment, organisational, educational, economic, social, technical, cultural, and legal factors on the capacity building of extension experts in the development of climate smart agriculture. The research is applied in terms of its objective and descriptive survey in terms of its method. The results showed that infrastructural factors had the highest impact on climate-smart agriculture with 22%, followed by organisational factors with 21%, social factors with almost 17%, cultural factors with 16.5%, educational factors with 13%, legal factors with nearly 13%, technical factors with 11.4%, and, finally, economic factors with just over 10%. Overall, the factors under study explain more than 68% of the variance in climate-smart agriculture (R2 = 0.687), indicating the impact of the aforementioned factors on the development of climate-smart agriculture. Also, the design of this model made the promoters, by identifying and increasing the potential and capacities of themselves, villagers, and farmers, to be able to adapt themselves to climate change and drought and, by increasing resilience and adaptability, to have positive effects for the development of sustainable agriculture. Along with increasing the product, it will increase the income and reduce the costs for them.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".