The simplified asset management plan for a sustainable future: indonesia’s irrigation systems
Bibliographic record
Abstract
Agricultural irrigation plays a fundamental role in ensuring the food security and economy of rural communities.However, irrigation systems in many developing countries suffer from low performance, which eventually adversely affects their sustainability.To gain a more in-depth understanding of performance and sustainability in existing Indonesian irrigation systems, a set of assessment was conducted.This incorporated methods of Rapid Appraisal Process and Benchmarking, an opinion survey, and an asset survey.A triple-bottom line (TBL) sustainability assessment was also conducted to determine the levels of sustainability and performance shortfall, as well as their causes.Based on the results, a set of physical and managerial changes were proposed to improve irrigation system performance and sustainability.The viability of the proposed changes was assessed further through a stakeholder's opinion survey and against three key sustainability issues of a TBL sustainability indicator framework: technical and economic, social, institutional and legal issue, and environmental, public health and safety.The purpose was to fi nd an alternative and robust solution that was also preferred by stakeholders.Eventually, a simplifi ed asset management planning (AMP) model was developed, which would enable water user associations to implement independently and easily.The AMP, consisting of budget planning and short-term planning, was also based on the robust preferred priorities of improving irrigation performance and sustainability.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".