Farmer's Opinion on the Usefulness of Agro-advisory Services in the NICRA Operated Districts of Odisha
Bibliographic record
Abstract
Agro Advisory Services (AAS) play a dominant role to minimize crop losses and increase crop production and productivity. AAS provides basic, timely and accurate pre-information of diff erent climate and weather conditions for diff erent crops. With this milieu, National Innovation on Climate Resilient Agriculture (NICRA) is operating in fi ve climate vulnerable districts of Odisha. In this study three districts were selected based on the diff erent agroecological situations. Diff erent advisory services like agro-advisory, weather advisory, the usefulness of news letter, farm literature and farm videos. Agro-advisory services were found more than half of the farmers found it highly useful while 20 per cent found it useful. The scenario is much better in NICRA districts in comparison to non-NICRA districts (p=0.006). But in weather advisory, 35 per cent of farmers found it was highly useful while 15.8 per cent of farmers found it was useful. About a quarter (26.7%) of farmers expressed Newsletter is highly useful whereas 21.7 farmers opined it is useful. About 23.3 per cent of farmers found farm literature was highly useful while 25.8 per cent found it useful. Only 10 per cent of farmers opined that farm videos are highly useful whereas 18.3 per cent told it is useful. In the Non-NICRA areas, 33.3 per cent see these services as highly useful or useful. This scenario is signifi cantly better in NICRA areas (p=0.008).
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".