PERANCANGAN SMART VERTICAL GARDEN SEBAGAI STRATEGI MENINGKATKAN RUANG HIJAU DAN KENYAMANAN TERMAL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Balikpapan, located in East Kalimantan Province, Indonesia, is experiencing rapid growth accompanied by increasingly complex environmental challenges, including the effects of climate change. Data shows a rise in air temperature, impacting not only the outdoor environment but also the indoor thermal conditions of public buildings such as offices, shopping centers, and educational institutions. At Institut Teknologi Kalimantan (ITK), the increasing demand for air conditioning systems reflects the direct impact of global temperature rise, resulting in heightened energy use and greenhouse gas emissions. In response to these issues, this research explores the design and implementation of smart vertical gardens as an innovative solution to enhance thermal comfort and energy efficiency. The smart vertical garden utilizes shading plants, sensor technology, and automation to reduce a building’s carbon footprint while improving thermal comfort. Building B at ITK is chosen as the case study due to its function as a hub of academic activities, making it a strategic location for implementing this green technology. The research adopts a comprehensive approach, including literature review, empirical data collection, thermal analysis, simulation, and design. The findings demonstrate the effectiveness of the smart vertical garden in reducing cooling energy demand, improving thermal comfort, and promoting campus greening. The implementation of this technology has the potential to serve as a sustainability model for public buildings. The results of this study provide valuable insights for academics, practitioners, and policymakers in developing green strategies and advancing sustainable development.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it