Green spaces in Bucharest - present situation, current developmental programs and future aspirations
Bibliographic record
Abstract
The following article aims to review the situation of the green spaces in Bucharest, by going through a case study with the goal of emphasizing crucial aspects for this city to become “smarter” than it currently is, in terms of sustainable development. This article is based on geophysical data and urban characteristics of this capital, compared to the other major European cities. We also present some of the previously proposed governmental initiatives in terms of natural spaces and lifestyle improvement, as well as what citizens believe to be improvements of their current living conditions. Through our research, we found that Bucharest possesses various sectors with a large demographic index. These condensed housing sectors, usually involving tall apartment buildings, could benefit from small parks, as well balcony or “vertical” gardens. Considering the great number of schools of this capital, green initiatives can be implemented in educational set-ups as well. Implementing previously proposed ideas such as the creation of a “green belt” would significantly improve the air quality, landscape and pedestrian security of the busy Bucharest. After all, maintaining a green and healthy urban area brings major benefits, and it should be a common goal for all its citizens. Besides the general public, this review article can be of particular interest to the city council and to researchers interested in civil engineering and urban development. Lastly, we strongly believe in the importance of the present study, since it contains up-to-date information and it customizes sustainability initiatives to the economical and social conditions of this city.
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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 | 0.001 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".