Evaluation and Optimization of Models' Priorities in Redesigning of Urban Parks in Semi-Mountainous Areas
Bibliographic record
Abstract
With the growing trend for semi-natural spaces in urban areas, urban parks and the related social behaviors have been faced with more interest. However, the flaw in the original design, changes in the park field and variation in living standards level of users, causes situation that lead to some inappropriate performance functions of the parks or parks unable to provide optimal quality of these functions. The main objective of this study was to address these failings and to advance the cause, using problem solving method proposed in this social-architectural research. In this context, semi-mountainous areas parks, review as case studies due to the nature of subject and locating in the particular region in the country for evaluation the priority of redesigning principles.To achieve this goal, redesigning in urban parks is defined and after studying worldwide experience and situation analysis, by using a qualitative evaluation in an urban park in Khoram-Abad, Arak and Hamadan, redesigning principles is proposed. The research method was descriptive-analytical and information collecting method have been taken by theoretical studies through library method and field studies including survey of the physical condition of the park and picked up a case study and statistical distribution of questionnaires among 224 presented persons in the park. The statistical analysis was performed on the responses of consumers by analysis program R and the frequency response is obtained. To assess the principles' priority of redesigning in the city's parks, the covariance was used.The results show that the three principles: consideration to the union, linkage and coordination between the initial layout and redesigning plan of parks, optimizing the ecological functions of Park and optimizing social functions of Park through the relevant mechanisms should be in interest and commitment in redesigning parks of semi-mountainous areas.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".