Glass Properties Selection Effect on LEED Points for Core and Shell High Rise Residential Building in Jordan
Bibliographic record
Abstract
Green building concept is flourishing in developing countries due to the high-energy cost. To meet the green building requirement, this paper investigate the effect of glass properties on 4 and 20 story building total, heating and cooling, loads for variable glass-wall ratios using HAP4.5 . The ratios considered are 20%, 40%, 60%, 80% & 100% glass to wall. The simulations were carried out for both building under moderate climate, Amman, Jordan was the selected city from this climatic zone. The glass overall heat transfer (U-value) & shading coefficient (S.C) are paired in nine combinations, these pairs are called Combination Number (CN). Combination Number simulate all range of glass properties, CN 1 is the worst case to 9 the best glass properties. Results showed that the minimum impact of glass properties was on 20-percentile glass to wall ratio and maximum at 100 Percentile. On the other hand, a Maximum of 10 LEED point could be achieved in the 20-percentile and 16 point in the case of 100-persentile for both 20 and 4 story building. Data found in this paper where formulated in the following equations with R2 = 0.949. 4 Story Total Load = 915514 - (63546 * CN) + (9770 * Glass to wall) 20 Story Total Load = 7638346 - (540386 * CN) + (84090 * Glass to wall)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".