Water Conservation for Livelihoods and Labour Constraints: A Case Study from Nepal
Bibliographic record
Abstract
Cash crop production, increased demand for water and high workloads are raising concerns about the sustainability of intensive farming systems in Nepal. Water conservation technologies are seen as a potential option for producing off-season cash crops, and reducing water demand and labour for water collection. Their appropriateness was evaluated from biophysical and social perspectives by combining hydrometric monitoring, gender and water use surveys and field trials of drip irrigation systems. Results demonstrated domestic and irrigation water shortages prevalent from March to June, and an increased demand for water as farmers move towards market-based production. Women’s workloads were high, 13.5 hours per day, necessitating labour reduction as a condition for small-scale water projects. Low cost drip irrigation trials quantified high water use efficiency under a deficit water regime, and capital costs could be paid off in the first crop. Labour was a significant component of variable costs making efficient technologies attractive as demonstrated by the 100+ systems adopted in the watershed since the trials in 2001.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".