Bibliographic record
Abstract
Two fundamental design strategies should be taken into consideration when designing a residential building in desert climates, they are as follows: minimizing solar heat gain through shading and proper building envelope and maximizing passive cooling through natural ventilation. By introducing extensive vegetation yet carefully distributed, shading of building’s facades or roofs will directly mitigate heat gain through building envelope. An eco-house was designed in Abu Dhabi with special attention to greenery. In this study, landscape elements were intensively analyzed with the aim of reducing heat gain and improving overall building energy performance. Landscape elements such as green roofs, grass ground cover and greenery next to external walls were simulated in order to achieve optimum energy performance. The use of outdoor landscape (grass ground cover and shade trees) has made a 9% improvement of performance over the reference case regarding the electrical energy use and greenhouse gas emissions. The energy use of the house dropped down by 16% for cooling and 18% for fan operation. With regards to the green roof scenario, a performance improvement of 19% over the base case has been achieved. The energy use of the house dropped down by 24% for cooling and 27% for fan operation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".