CONSTRAINTS EXPERIENCED BY MUSHROOM GROWERS IN ADOPTING IMPROVED MUSHROOM PRODUCTION PRACTICES IN PUSA, BIHAR
Bibliographic record
Abstract
Mushrooms (vegetarian meat/vegetable beef stick) is becoming fast popular because of its short time period between cultivation and harvesting; less initial investment and can be grown with locally available resources. Though more technology is available for boosting mushroom production, the yield so far achieved is not high. Hence, an attempt is made to find out the constraints faced by mushroom growers in adoption of improved mushroom production practices. Samastipur district of Bihar was purposively selected for the study because, Dr. Rajendra Prasad Central Agricultural University, a pioneer in mushroom production technology is located in the study area. 120 respondents from six villages of Pusa, Samastipur, Bihar forms the respondents of the present study. Primary data was collected from the mushroom growers and the responses were subjected to statistical analysis. The results indicated that unavailability of quality spawn, unavailability of skilled labor, absence of technical guidance, high transport cost, unavailability of storage facilities, high cost of spawn and long distance market were the most problematic constraints faced by the mushroom growers. Thus, it can be concluded that adequate extension service should be made available to make mushroom cultivation popular, market and marketing of the products.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".