Study on Cultivators Associating Post Harvest Losses of Onion Vegetable in Sindh’s Mirpurkhas District
Bibliographic record
Abstract
A study was carried out in Mirpurkhas District of Sindh Province during the year 2015-16, aiming to observe post-harvest losses of onions associated with the cultivators. Following the random sampling 60 respondents were selected from 12 villages of 06 Talukas in the District. Interviews were conducted for the collection of data. Problems expressed were as; high cost of fertilizers (93.33%), high cost of pesticides (93.33%), hand weeding is labour consuming and expensive (91.66%), labour problems during harvesting (85%), ineffective and costly weedicides (80%), lack of knowledge about recommended fertilizers doses for onion crop (80%) and lack of knowledge about improved varieties (68.33%). The problems in marketing of onion include lack of remunerative price (96.66%), fluctuation in market price (93.33%) and high charges of transportation (20%). The present study suggested that training/ awareness programs should be conducted for cultivators regarding establishing technical storage and handling onion problems.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".