Performance of World Health Organization as a Green Building
Bibliographic record
Abstract
The rapidly growing world energy use has already raised concerns over supply difficulties, exhaustion of energy resources and heavy environmental impacts. The global contribution from buildings towards energy consumption has steadily increased. Jordan relying on importing more than (97%) of its oil needs. The household in Jordan consumes 43% of the total electricity produced. The current situation enforces us to have more efficient use of energy in this sector. For this reason, energy efficiency in buildings is today a prime objective for energy policy at national and international level. The Jordanian buildings codes such as the Jordan green building code were developed to face energy challenges that Jordan has recently encountered. In residential sector, energy is used for equipment and appliances that provide heating, cooling, lighting, water heating, and other household demands. In this study, an efficient energy building has been selected and studied. The present study concentrates on the one of the energy saving examples, which is Green building represented in the World Health Organization (WHO) building in Amman. A comprehensive study of energy consumption in the building has been carried out. A comparison between the Jordanian regular buildings and the current building was made; EnergyPlus was used to make all calculations. It is found that the WHO building saves 23.9% of the total energy saved from HVAC systems, and widely dependent on the natural lighting. WHO reduces the Greenhouse gases emissions of CO2, about 57563.12 kg of CO2 were reduced, which helps in the global warming.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".