Effects of vertical green technology on building surface temperature
Bibliographic record
Abstract
a sustainable technology for improving the energy efficiency of buildings is the use of urban greening in order to reduce the energy consumption for air conditioning in summer and to increase the thermal insulation in winter. a worldwide growing interest in urban green is encouraging the application of the greening technology for more sustainable buildings. building indoor air temperature depends on several different parameters related to the climate of the region, the building itself and its use. the main parameters influencing the microclimate are: external air temperature and relative humidity, incident solar radiation, long wave radiation exchange between the building surfaces and its surroundings, wind velocity and direction, air exchanges, physical and thermal properties of the building's envelope materials, design variables such as building dimensions and orientation, presence of artificial light, electrical equipment. green faades can allow the physical shading of the building and promote evapotranspiration in summer and increase the thermal insulation in winter. External wall surface temperature is a parameter useful to assess the effectiveness of green faades. an experimental test was carried out at the university of bari (italy) for three years. three vertical walls, made with perforated bricks, were tested: two were covered with evergreen plants (Pandorea jasminoides variegated and Rhyncospermum jasminoides) while the third wall was kept uncovered and used as control. several climatic parameters concerning the walls and the ambient conditions were collected during the experimental test. the experimental data were used for developing a multiple regression equation regarding the dependence of the difference of external surface temperature between the green faades and the control wall and the weather conditions. the model shows a good predicting ability.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".