Analysis of Water Shortage and Socioeconomic Impacts on Jujube Growers of Taluka Hyderabad Rural, Sindh Pakistan
Bibliographic record
Abstract
Water plays a vital role not only for survival of human being but it is also important for crops, animal and every creature which lives on the universe. Therefore; water shortage has some negative impacts on socioeconomic condition of jujube growers. Jujube (Ziziphus jujube) locally called ‘Beer’, is a native fruit of South Asia. Produced in moderate regions of different countries in the world: such as China, India, Pakistan, Syria, Malacca, Australia and Malaysia, Afghanistan, Iran and Russia. China is perhaps the most important country for jujube cultivation, where it is known as the “Chinese dates”, with hundreds of varieties, some being seedless. the study was conducted at Taluka Hyderabad Rural. Samples were randomly carried out from six villages (ten growers from each village) were selected, so the total sample size was 60 in numbers. Results exposed that education level of growers were primary 48 percent, secondary 27 percent, higher 18 percent and illiterate 7 percent respectively. Pattern of farming of growers in study area states that majority 29 percent of producer’s were full time and 71 percent of respondents were part time engaged in jujube growers. Mostly 67 percent of jujube farmers belong to medium income group, 18 percent were high income group and 15 percent were very low income group. Canal water unavailability to growers was 43 percent in study area. So government should take action to provide them excess of water for earning maximum profit
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".