Urban Resilience Strategies: Creating Adaptable and Sustainable Public Spaces Along Riverbanks in Mid-Sized Cities – A Case Study of Kut City
Bibliographic record
Abstract
The research uses Kut City as a model to examine urban resilience strategies in designing public spaces along riverbanks in medium-sized cities.It aims to promote sustainability, respond to climate change, and support biodiversity by creating flexible and adaptable public spaces.It also seeks to stimulate economic and recreational activities along the banks of the Tigris River, enhancing the city's quality of life, which can be generalized to other mediumsized cities. Integrating urban resilience with public space design to provide sustainable environments resilient to environmental and social challenges is also an area of exploration.The offered model improves urban resilience and sustainable expansion by developing adaptable spaces for climate change.The model can facilitate societal engagement in public spaces, donating to sustainable urban maturation by developing biodiversity and socialeconomic sustainability.The results revealed the significance of collaboration among the public, private, and local sectors in investing in green infrastructure projects and enriching local environmental sustainability.They also confirmed that enhancing permeability between urban spaces and river banks improves social relations and contributes to devising more sustainable urban environments.The study also recommends adopting flexible urban planning policies to be compatible with future climate change, focusing on allocating land for multifunctional green spaces, enhancing public transportation networks to facilitate access to river banks and safe areas, and improving environmental permeability through green passages that connect river banks to the city center, which enhance the overall comfort for residents and reducing temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".