Sustainable-Eco-Buildings Assessment Method SEBAM for Evaluation of Residential Areas in Hot-Dry Climate
Bibliographic record
Abstract
This research aimed to investigate the present situation regarding residential neighbourhood in hot-dry climates. The area of study comprised four urban classes in Greater Khartoum. The problems of residential buildings were examined, aiming to find a sustainable assessment method for evaluating residential areas and their services The methodology of the research began with a literature review for identifying passive and sustainable solutions suitable to hot-dry climates. This method employed eight main categories: sustainable sites, indoor environmental quality, outdoor thermal control, building forms, materials and resources, water supply, power supply systems, and environmental plan processes and CO2 emisions. In addition, a points scale was used, based on ratings of ‘Excellent’, ‘V. Good’, ‘Good’, and ‘Pass’, with a total of 125 points to determine the evaluation result for a building. The study evaluated an urban sample in the Al Taief neighbourhood. A survey was initiated by identifying the standards for selecting the case study, the survey studied 48 cases in the residential areas, analysed the collected data, and then summarised it into tables and figures. The results presented indicated that 19% were Good, 25% were Pass, and 56% were considered ‘weak’. The conclusions and recommendations regarding urban housing services can be applied to sustainable ecological neighbourhoods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".