Applicability of Managed Aquifer Recharge to achieve the goals of sustainable development facing climate change in semi-arid regions (Southern Spain)
Bibliographic record
Abstract
In 2015, “The Sustainable Development Goals” (SDGs) were adopted by the United Nations as a call to action to protect the planet and natural resources. SDG number six deals with guarantee the availability of water and its sustainable management by 2030. Some of the main problems in water management are the decreasing annual rainfall and the expected rise of rainfall intensity and evaporation rate as a consequence of climate change, together with the growth of water demand. All that is particularly alarming in semi-arid regions and other domains, like the Mediterranean area, where water resources are very often dramatically stressed. In this scenario, Managed Aquifer Recharge (MAR) is considered an increasingly important water management strategy to enhance the quantity and quality of groundwater as a key step towards achieving the SDGs. The selection of MAR techniques and the effectiveness of their design and implementation require a complete understanding of the physical/hydrogeological framework. This must include the definition of aquifer geometry and limits to estimate storage capacity, the assessment of hydraulic properties, the quantification of the groundwater budget, the establishment of reliable hydrogeological conceptual and numerical model, and the groundwater quality monitoring. Besides, the availability of water to be recharged is crucial. Many regions use stormwater (Perth, Australia) and wastewater (Alberta, Canada) to recharge aquifers via infiltration basins or wells and subsequently reuse this water for drinking use or irrigation purposes. Finally, social and economic drivers must also be taken into account.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".