Benchmarking Case Study, Applying Sustainable -Eco - Building Assessment Method (SEBAM) in Greater Khartoum, Comparing with Global Systems
Bibliographic record
Abstract
This sample was taken as benchmarking in (Abdelmoneim, H., 2019) which has got the higher points comparing with all the other samples studied in this research, is chosen for global assessment approach an excellent solution for the sustainable-eco-building assessment method (SEBAM). This study aims is to present and analyses a benchmarking case study for sustainable eco-buildings in Greater Khartoum is the capital of Sudan, one of The greatest countries in north Africa. The methodology is applying global assessment method to this case study, which is LEED, BREEAM, AGBC, ESTIDAMA and GSAS then compare the result with the local system came up of (Abdelmoneim, H., 2019), sustainable eco-building assessment method to evaluate residential buildings (SEBAM) to justify the results. The comparison is done in this research in the main categories and results. The outcomes show differences rather than similarities this will be discussed in the paper and come up by conclusion and recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".