Evaluation of promising technologies for soil salinity amelioration in Timpaki (Crete): a participatory approach
Bibliographic record
Abstract
Abstract. Soil salinity management can be complex, expensive, and time demanding, especially in arid and semi-arid regions. Besides taking no action, possible management strategies include amelioration and adaptation measures. Here we apply the World Overview of Conservation Approaches and Technologies (WOCAT) framework for the systematic analysis and evaluation and selection of soil salinisation amelioration technologies in close collaboration with stakeholders. The participatory approach is applied in the RECARE (Preventing and Remediating degradation of soils in Europe through Land Care) project case study of Timpaki, a semi-arid region in south-central Crete (Greece) where the main land use is horticulture in greenhouses irrigated by groundwater. Excessive groundwater abstractions have resulted in a drop of the groundwater level in the coastal part of the aquifer, thus leading to seawater intrusion and in turn to soil salinisation. The documented technologies are evaluated for their impacts on ecosystem services, cost, and input requirements using a participatory approach and field evaluations. Results show that technologies which promote maintaining existing crop types while enhancing productivity and decreasing soil salinity are preferred by the stakeholders. The evaluation concludes that rainwater harvesting is the optimal solution for direct soil salinity mitigation, as it addresses a wider range of ecosystem and human well-being benefits. Nevertheless, this merit is offset by poor financial motivation making agronomic measures more attractive to users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".