A proposed rating system for: touristic communities in Egypt
Bibliographic record
Abstract
Rating systems has started to be a tool to manage and asses the performance in many countries in the world.Since the touristic communities are considered one of the critical markets in Egypt, therefore, the reliability of tourism sector requires enough means to improve the performance of these communities.The research objectives is to design a rating system that assist developers to enhance the quality of the existing and planned communities following the guidelines ofsite and urban development, green infrastructure, efficient energy, green transportation and sustainable tourism.The research method is to compare, adapt, and apply the most representative community environment assessment schemes that are in use today.A preliminary studyof six different community rating systems (LEED-ND, Pearl, STAR community, BREEAM, IGBC and GSAS) took placeon the urban level, and followed by analysis and design of a new rating system which is introduced and applied on the city of Sharm EL-Sheikh.The newEgyptian Rating System for Touristic Communities (ERSTC) achieved better environmental, social, and economic performance compared to other rating systems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".