Counteract Soil Consumption through Ecosystem Services and Landscape Restoration for an Efficient Urban Regeneration
Bibliographic record
Abstract
Soil consumption, marked by the expansion of artificial land cover for residential, productive, and infrastructural purposes, is a concerning trend in Italy, as revealed by the Copernicus land monitoring program. The issue is exacerbated by agricultural intensification and urbanization, particularly affecting regions like Lombardia and Piemonte. However, Sicilia, Abruzzo, and Lazio experience notable increases in processes of abandonment and re-naturalization. Data from Ispra highlights the need for in-depth study, especially in regions like Sicilia, where contrasting phenomena occur. This study utilizes Ispra data to monitor and formulate strategies for mitigating soil consumption and safeguarding ecosystem services. The research aligns with objectives related to combating climate change and facilitating the ecological transition of territories. The complexity of land consumption, influenced by interdependent factors, is evident in the achieved results. Effective strategies for containment and re-naturalization involve the implementation of town planning regulations and multi-level behavioral pathways. This study aims to identify contextual actions that can reduce land consumption, promote de-impermeabilization, and encourage re-naturalization, focusing on enhancing ecosystem services in land use activities. Thus, it focuses on understanding the contributions of ecosystem services, landscape restoration and green infrastructure on climate mitigation, and a reduction in land consumption in urban regeneration processes. As well, through open-source systems, it is important to monitor in real time the trend of the quantity of factors and variables and the state of the environment, and the reasons to intervene with systemic strategies and actions constitutes another lens of focus.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".