SWOT Analysis of Reclaimed Water Use for Irrigation in Southern Spain
Bibliographic record
Abstract
The EU project ‘Network for effective knowledge transfer on safe and economic wastewater reuse in agriculture in Europe (SUWANU-Europe)’ aims to identify the limitations and factors of success in fostering the use of reclaimed water by the agricultural sector in different European regions. This study shows the results of a SWOT (Strengths-Weaknesses-Opportunities-Threats) analysis in the case of Andalusia (Southern region in Spain). The goal is to define a regional strategic plan to promote the use of urban reclaimed water for irrigation purposes. The SWOT analysis carried out in this study has identified barriers and challenges that still exist in the implementation of irrigation systems with reclaimed water. Among the main threats identified, stakeholders’ perceptions and the higher cost of reclaimed water for irrigators (compared to alternative sources) play a relevant role. Additionally, the excessive bureaucracy and long administrative processes are significant weaknesses to be considered. On the other hand, technology availability and the increasing scarcity of conventional sources are seen as strength and opportunity factors for the expansion of reclaimed water use for irrigation purpose
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".