Effect of Green Areas Density on Real Estate Price in Ramadi City
Bibliographic record
Abstract
Urban green spaces are among most important indicators of amenities, which include providing entertainment, aesthetic enjoyment and environmental quality.However, most of these values lack their relationship to market prices, which leads to gradual overruns of those areas through urban sprawl.As a result, there is a need to obtain quantitative information on implicit and non-market advantages and benefits of urban green areas that have been classified in Ramadi into three categories.Main procedure is to integrate real estate that is bought and sold on market with convenience values and that people are willing to pay money to live near local comfort environment.Statistical models that stakeholders use can then be used to identify those green environmental facilities.It should be possible to determine monetary value of benefits provided by green spaces.Research was conducted in city of Ramadi, which is experiencing a large difference in real estate prices, which facilitates application of statistical models to evaluate green spaces.Results clearly affirmed positive effects of green spaces in urban areas on values of neighboring homes, and highlighted preferences of investors and residents alike.Independent variables were area of green space, distance from green scene, duration of access to park and public green space, and percentage of urban green areas.Moreover, variables of land use intensity and density of educational environment proved to be of great importance in model.As a result, recommendations provide insight into stakeholders and policy makers involved in urban planning.
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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 | 0.001 |
| 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 | 0.001 |
| 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".