The Socio-Ecological analytical framework of water scarcity in rafsanjan township, Iran
Bibliographic record
Abstract
Ground water scarcity is a main socio-ecological challenge in the Middle East.While ground water reserves seem vast, the impacts of over-exploitation and inadequate control over water consumption may threaten the sustainability of aquifers.The signs of aquifer depletion and its influence on water accessibility have become apparent in recent years.Using the case of Rafsanjan Township, Iran, this study aims to understand the socio-ecological factors and their inter-relationships in driving and exacerbating the water crisis situation, the ongoing policy responses and the possible consequences of current trends.The Drivers, Pressures, State, Impacts and Responses (DPSIR) framework, developed by the European Environmental Agency in 1999, is used to analyze the components of the socio-ecological system.Inputs are generated through a time series analysis of Landsat images, extracted spatial datasets, secondary literature and government reports.This study illustrates the conflict between rapid economic development policies that have simulated the expansion of pistachio orchards on the one hand and sustainable water resource management on the other.Some responses based on a long-term socio-ecological resilient planning approach may provide a more sustainable perspective, but will require a substantial rethinking of current policies, improved water management practices, and additional research.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".