Comparison of bankruptcy methods in the operation management of the Karkheh River Basin to allocate more water to the Hawr-Al-Azim Wetland
Bibliographic record
Abstract
Abstract Bankruptcy methods are straightforward practical methods to address the problem of allocating limited sources of water to different users in regions where claims exceed assets. In the present study, three levels of restoration for the Hawr-Al-Azim wetland (35, 50, and 100%) and two agricultural-related scenarios, maintaining the current cultivation area and decreasing the area under cultivation, were considered and assessed using classical bankruptcy methods. The results showed that, because of climatic conditions and agricultural demands, full wetland restoration was out of reach and led to minimum satisfaction levels for agricultural beneficiaries. The results also showed that the modified bankruptcy method led to the highest satisfaction levels for beneficiaries in the scenario of maintaining the current cultivation area. In addition, the percentage of the water supply was increased by applying the scenario of crop restriction in the conditions of the full restoration of the wetland; for example, in the Abbas Plain region, this increase was achieved by almost 10–15% in all methods. On the other hand, decreasing the area under cultivation shifted the allocation problem in the basin to a non-bankruptcy one.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".