Urban Greening Strategies for Compact Cities, An-Najaf Historical City, Iraq, A Case Study
Bibliographic record
Abstract
Urban greening is a crucial trend for achieving sustainability as it helps enhance the local climate and lower temperatures.So interesting green elements in cities is an urgent necessity, not an option.This study tries to elucidate suitable urban greening solutions in cities compact and limited in space.This study aims to clarify relevant urban greening strategies in densely populated cities with limited space, particularly old historical cities, by employing a methodology that involves formulating effective and suitable indicators to enhance urban greening in such places.The findings of the theoretical framework revealed various approaches to implementing urban greening in densely populated cities.These include the establishment of a network of green spaces, the installation of green roofs, the incorporation of front balconies, the use of temporary vegetation, and the creation of gardens and parks outside the city.These strategies were assessed using descriptive methods.These indicators were implemented in the ancient city located in the Al-Najaf Governorate, which is a historically significant city and a crucial hub.The implementation of these strategies can enhance the local climate of the city and transform its roadways and structures in a sustainable manner.
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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 | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".