Green and Smart Urban Development: A Comparative Studies Between Cities of Romania, Canada and Denmark
Bibliographic record
Abstract
Abstract Due to the fact that the planet’s resources are limited and human exploitation has led to unprecedented environmental pollution, sustainability has become a concept of great importance in recent years, especially in the context of very rapid and large-scale urban development. The green city is a form of sustainable city focused mainly on the creation of green spaces, which helps, among other things, to reduce pollution, to combat climate change and to create a more favorable environment for people. Green infrastructure is the main element that characterizes this type of sustainable city, the dynamics of the use of the term in specialized studies showing an upward trend. Interest in the notion of green city has seen a major increase in the last 8 years, highlighting the need to create a more nature-friendly way of urban development. The country that stands out regarding its contribution in terms of studies carried out on the theme of green city is China, while Romania is one of the countries where this subject is very little researched. A cluster analysis of cities in Romania, Denmark and Canada provides a valuable perspective, namely that Romanian cities are the most polluted and have very few green spaces per capita, suggesting the existence of problems with government policies to transform the cities into ones that respect the environment more.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".