Recalculating the sustainability criteria within the LEED system according to the Syrian construction conditions using the FAHP method
Bibliographic record
Abstract
Background: With the global trend to develop the construction industry and achieve the sustainability of resources, a set of systems have emerged to assess sustainable buildings, the most important of which are the Environmental Efficiency Rating System (BREEAM) in the United Kingdom, the LEED method for evaluating sustainable buildings in the United States, the Green Globes Rating System in Canada, and the ESTIDAMA Pearl Rating method. In the UAE, the Green Pyramid Rating System in the Arab Republic of Egypt and many others. Methods: This study determined the main standards and their relative weights included in the American LEED system and then re-weighted according to the Syrian construction conditions using a (fuzzy analytic hierarchy process) (FAHP). Results and Conclusion: The study showed that it is impossible to find a stable and effective evaluation system at every time and place due to the different construction conditions, economic situation, and priorities between one country and another, as well as the difference in climatic conditions between one region and another. Although the researchers used the same main criteria adopted in America, the results in Syria differed. This reflects the local situation in the study area (Syria). Keywords: Sustainable Buildings, Sustainability Assessment criteria, LEED System, Fuzzy Logic, FAHP Method.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".