The Economic Impact of Climate Change on Optimal Allocation of Water Resources in Agricultural Sector (Case Study: Sarbaz River Basin of Sistan and Baluchestan Province)
Bibliographic record
Abstract
Agriculture as the largest user of water resources and providing for human needs, water resources management is faced with many challenges. On the other hand, climate change and weather conditions resulted in the production of agricultural products subject to this change. Scarcity of water resources due to reduced precipitation patterns change and global temperature has increased agricultural production and food supply is affected. Due to the adverse effects of climate change on various sectors of production, ecological and human communities, of climate change could be one of the most important environmental challenges mentioned century. Sistan and Baluchestan Province, one of which, unfortunately, has witnessed successive droughts and livelihood and economic problems it has caused, therefore, water management and determine an optimal cropping pattern is consistent with climate change; step is useful for planning and development of the agricultural sector. The aim of this study is to investigate climate change in the southern zone using the GCM, the effects of climate change (precipitation and temperature) crops into the region and a consistent pattern of socio-economic optimization using fuzzy multi-objective programming model is presented.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".