Assessment of Mobile Voice Agricultural Messages Given to Farmers of Cauvery Delta Zone of Tamil Nadu, India
Bibliographic record
Abstract
The study describes the assessment of mobile phone based agricultural voice messages disseminated to farmers of Cauvery delta zone in Tamil Nadu, India, during September, 2012 to June, 2013. The present study was conducted in July, 2013 for about 20 days period through telephonic interview using a well structured questionnaire with randomly selected 200 farmers across five districts (Thanjavur, Nagapattinam, Thiruchirapalli, Thiruvarur and Cuddalore of Cauvery delta zone in Tamil Nadu, India). The survey results showed that majority of the farmers have adopted the agricultural information disseminated through their mobile phone. In addition, Chi square analysis showed that the farmers with irrespective of socio economic characteristics such as gender, age, education, land holding and farming experience have adopted the agricultural information. More than half of the farmers had expressed that either all or most of the agricultural information were useful. Majority of them had expressed the information received on their mobile phone were trust worthy. With reference to satisfaction level of farmers, almost all the farmers were very satisfied with audio quality, simplicity of language and contents of voice messages. Majority of the farmers’ have indicated that the mobile voice messages were of better as compared to other sources of information that they were accessing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".