An Application of WEAP Model in Water Resources Management Considering the Environmental Scenarios and Economic Assessment Case Study: Hirmand Catchment
Bibliographic record
Abstract
Hirmand catchment is one of the important trans boundary catchment in Iran. Sistan people living in south east Iran related to this catchment. Meanwhile drought of the last two decades, due to dust storms jeopardized the Human health. Also, causing destroyed vegetation cover and animal habitat in region. The first objective of the present study is water resources management under environmental scenarios using the WEAP model in Hirmand catchment. In study, dust stabilization and animal-plant sustainable ecosystem scenarios have been applied. The second objective is economic assessment of defined scenarios. According to the results, average total demand in dust stabilization and animal-plant sustainable ecosystem scenarios increased about 238 and 231 million cubic meters compared to the current account, respectively. Also, unmet demand compared to the current account increased 193 and 200 million cubic meters, approximately. According to the economic assessment calculations, benefit in dust stabilization scenario 160 milliards rials and in animal-plant sustainable ecosystem scenario 209 milliards rials decreased in agriculture sector. Therefore, despite the decline in benefit, in policy making and water resources management should be special attention to protect the environment wetlands.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".