Water Policy Under Risk and Uncertainty A Dynamic Evaluation Model of Fodder Cultivation in Oman
Bibliographic record
Abstract
The continuous cultivation of the Rhodes Grass in Batinah costal area and Salalah region of Sultanate of Oman has a negative impact on the overall agriculture system and production. Improvement of the conditions could be achieved by introducing new water policy into farming and using Government supporting tools to motivate farmers and achieve financial sustainability. The new water policy and strategies formed by Government are examined in three cultivated locations in this paper: Salalah location with enough irrigation water, Hanfeet location with low irrigation water and Dawkah location with very low irrigation water. Economic efficiency of the location is evaluated through the Net Present Value and IRR calculation. Within the assumption of the objective evaluation of input parameters, we can expect an acceptable economic efficiency of the investment only in Salalah location. The simplified deterministic evaluation of economic efficiency is formed to identify the relevant risk factors, followed by its quantification by the simulation processes. Taking the risk into account leads to a significant decrease of the economic attractiveness of stakeholders and more Government support is needed to achieve water policy and project sustainability at new location at Hanfeet and Dawkah location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".