Urban sustainability: Co<sub>2</sub> uptake by green areas in the historic centre of Siena
Bibliographic record
Abstract
Nature provides free assets and ecological services essential for human health and economic activity.For this reason, our ecosystems need to be protected and managed without affecting their integrity in the long run.The absorption of carbon dioxide (CO 2 ) by vegetation is one of the most important services provided by the ecosystem, which needs to be preserved over time, because it regulates the planetary energy and entropic balance.In the cities, population growth, together with progressive urbanization, often leads towards a reduction of green areas and related ecological systems.Therefore, urbanization processes should be planned, also keeping in mind maintenance of a right equilibrium between built and green areas.In this study, the green areas in the historic centre of the city of Siena (Tuscany, central Italy) were identified and investigated.It was found that the total surface area of the historic centre was 169.64 ha, of which 71.54 ha was occupied by valleys and other green areas.The real contribution of this natural heritage to the CO 2 absorption capacity of the ecosystem, was 330.50 t CO 2 yr -1 , depending on the vegetation types present in the green areas (e.g.trees, olive groves, vineyards, bamboo, grass and vegetables).Data showed remarkable carbon-storage efficiency untypical of a highly populated urban area (1.95 t CO 2 ha overall -1 yr -1 ).In an urban system, the presence of large green areas and their proper management are necessary to ensure its sustainability.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".