Assessing vertical green walls for indoor corridors in educational buildings and its impact outdoor: A field study at the universities of Canada in Egypt
Bibliographic record
Abstract
Developing Green Walls (GW) within the architectural realm involves integration of diverse functions to optimize the indoor performance of the GW, and outdoor in campus buildings. The methodology is fundamentally rooted in the selection and fulfillment of strategic incorporation of additional function-based GW design to various performance aspects through contextual design solutions. This approach is particularly crucial in the education sector, on utilizing GW to educate students as occupants in integrating gardening, ecology, botany, and beyond in their studies for decreasing the environmental pollution for new generations, to feel by environment indoor in accomplishing way for sustainability perceptions. The aim of the study is to assess the effective resources influencing vertical green walls needed for educational buildings to reduce the effect of indoor heat, to upgrade its air quality, simultaneously decreasing noise pollution to avoid the energy consumption. The Educational GW should be accessible easily, either by installing them naturally in accessible locations with appropriate heights or by designing the facilities to increase the student's integration with GW natural environment that will reflect on their academic progress and health. The study aims to assess the effect of green wall installation in the universities of Canada in Egypt campus in the heat gain reduction, energy consumption, CO2 emission reduction by using the Design-Builder simulation program to measure the effect of green wall installation in corridors and inner courtyard at the hottest period of the year (August 2:00 p.m.) and how it can decrease the cooling loads by 10.95 %:19.14 %, heating loads by 4.8 %:12.94 %, CO2 emission by 13 %:28.43 % and the monthly electricity bill by 35:41.5 % measuring the vertical greening cost in relative to short and long period as a needed sustainable technology to reduce building demand significantly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".