Studying Quality Factors of Townscape in Coasts Case Study: Joffre Neighborhood Center in Persian Gulf Coast of Boushehr
Bibliographic record
Abstract
Men’s tendency and requirement have been increased for being represented in natural areas in the city through industrialization and cities development. On the one hand, the necessity of surviving of urban damaged and dense areas and protecting and improvement of natural resources in cities and on the other hand, the townscape has been considered because of increasing the quality of urban spaces of environment in nature. Based on these views, surviving sea coasts, wharfs, green spaces, making connection among them and urban residential areas require coordination between environmental approaches and urban issues and solutions should be chosen to provide a balance among urban, aesthetic and ecology approaches. The present essay attempts to study the concept of townscape quality through descriptive-analysis method by considering Joffre coastal neighborhood center in western south of Boushehr as a part of coastal townscape and effective indicators on its quality and present solutions for promoting and improving the quality of coastal townscape in the studied part.
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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 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".