Performance and Efforts Regarding Usage of Mobile Phones among Farmers for Agriculture knowledge
Bibliographic record
Abstract
Nowadays everyone is using mobile phone for connect with each other’s and get information about different issues and updates around the world. However, mobile phones have also played an important role in the development of agriculture and make it easy for farmers to communicate with buyers and sellers to sell their goods in reasonable price. The study was conducted in Sindh province of Pakistan where ten districts were selected and 1500 farmers randomly were selected for interview. While 150 farmers were selected from these ten districts according to the list provided by Agriculture Department Government of Sindh and Sindh Irrigation & Drainage Authority (SIDA). Study showed that most of the farmers possessed their own mobile phone and more than half around 64% of the respondents directly call the buyers and negotiated to sell their goods. Moreover, 44.9% of the respondents neither agree nor disagree regarding mobile phone expanding their market information and 40.3% of the respondents take efforts and understand that mobile phones has improved their skills to communicate with buyers in market. However, due to lack of infrastructure still farmers are facing many problems in their working areas and have no proper information about pesticide use and weather updates in their agriculture areas. Government and non-governmental organizations should improve their skills and knowledge about agriculture product where farmers can increase their product in future.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".