Morphological Perspective of Urban Resilience Through Eco Urban Landscape: Iraq -Basra as a Case Study
Bibliographic record
Abstract
In the urban context, the sustainable static approach has evolved into a resilient orientation, determined by the dynamic urban characteristics of the cities, especially with the increasing challenges such as rapid urbanization, the emergence of different uses at the expense of urban green spaces, neglecting the ecological role that these spaces provide.The research starts from the need for an urban landscape that provides ecologically resilient contributions, it adopts the evaluation of urban resilience concerning the ecological urban landscape, by studying a part of the city center of Basra -Iraq.It aims to formulate guidelines that support the ecosystem by developing a multilevel theoretical framework based on the urban resilience principles to increase the ability of these spaces to innovate and stay strong during long-term change.The current study focuses on providing comprehensive knowledge about the ecological mechanisms and indicators that achieve resilience in the context of the urban landscape, and then answers the research question about the relationship between ecological urban landscape and resilience from a morphological point of view.We based on space syntax theory, using the analysis program (Depth map) to measure the morphological indicators through the main streets network, then analyzing their spatial relations with the green and open spaces as an integrated urban landscape to complete the evaluation and provide conclusions that can be used with other samples.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".