Urban green management plan: Guidelines for European cities
Bibliographic record
Abstract
Urban green areas are essential components of a city. They guarantee an adequate quality of life by providing several ecosystem services. Green areas must be designed and managed appropriately with a long-term approach ensuring a healthy urban ecosystem. It is possible to observe how especially in the USA and Canada there is a useful tool for this purpose, the Urban Forest Management Plan. The aim of this study is to understand which practical and effective plans were available for manage public urban green spaces in Europe, before COVID-19 (non-routine period), in order to carefully set up management plans. In order to reach the goal a bibliographic review was performed and reported following the PRISMA Statement. Furthermore, a research was carried out on the main management plans adopted by the municipalities in European capitals. In this regard, the research tries to investigate the knowledge base that European municipalities can use to set up an urban green management plan. The narration of the outcomes was designed as an initial guide aimed primarily at public administrators by providing them with a path and a scheme on how to structure a long-term green management plan in European cities. In the hope that even European municipalities can adopt a long-term green management plan, we propose a scheme to be followed to achieve this goal, with the indication of five essential points to 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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".