Accessibility of Pedestrian Rhythm in Public Spaces of Hillah’s Corniche Site, Babylon, Iraq
Bibliographic record
Abstract
This study analyzes the level of accessibility of riverine public spaces, as a fundamental component of sustainable and equitable urban structures, highlighting the Hillah Corniche as a practical model.The determinants of accessibility to riverine public spaces are based on their vital role in promoting spatial justice, urban vitality, and social sustainability in cities.The focus was exerted on a set of urban variables affecting accessibility, including: the spatial dispersion of public spaces, spatial proximity, means and modes of access, transportation and mobility, diversity of activities and facilities, and traffic flow.An analytical approach based on Geographic Information Systems (GIS) tools and descriptive spatial analysis of occupancy indicators and infrastructure efficiency was applied.The research was based on the main hypothesis that accessibility is a crucial factor in improving the efficient use of public spaces.The results showed that enhancing accessibility to public spaces depends extremely to a large extent on the equitable spatial distribution of these spaces, the integration of transportation networks, the multiplicity of access options, and the functional diversity within the site.The Accessibility Index (AI) was found 61.3%.To increase AI, many enhancements may be achieved, including high levels of vitality in these spaces, which requires designing highly efficient roads, providing organized parking spaces, and ensuring a safe and private environment that is compatible with the local social fabric.The study recommended activating the public transportation network, intensifying various activities within the spaces, and functionally integrating them with residential neighborhoods.This would contribute to increasing AI and occupancy rates and narrowing the gap in access to public space among different segments of society.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".