Natural ventilation in high-rise office buildings : an output of the CTBUH Sustainability Working Group : CTBUH technical guide
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
1.0 Introduction and Background 1.1 Historical Overview of Natural Ventilation in High-Rise Office Buildings 1.2 The Principles of Natural Ventilation in a High-Rise Building 1.3 Natural Ventilation Strategies 1.4 The Purpose and Benefits of Natural Ventilation 2.0 Case Studies 2.1 RWE Headquarters Tower, Essen, 1996 2.2 Commerzbank, Frankfurt, 1997 2.3 Liberty Tower of Meiji University, Tokyo, 1998 2.4 Menara UMNO, Penang, 1998 2.5 Deutsche Messe AG Administration Building, Hannover, 1999 2.6 GSW Headquarters Tower, Berlin, 1999 2.7 Post Tower, Bonn, 2002 2.8 30 St. Mary Axe, London, 2004 2.9 Highlight Towers, Munich, 2004 2.10 Torre Cube, Guadalajara, 2005 2.11 San Francisco Federal Building, San Francisco, 2007 2.12 Manitoba Hydro Place, Winnipeg, 2008 2.13 KfW Westarkade, Frankfurt, 2010 2.14 1 Bligh Street, Sydney, 2011 3.0 Design Considerations, Risks and Limitations 3.1 Thermal Comfort Standards 3.2 Local Climate 3.3 Site Context, Building Orientation and the Relative Driving Forces for Natural Ventilation 3.4 Planning and Spatial Configuration 3.5 Sky Gardens and Vertical Segmentation of Atria 3.6 Aerodynamic Elements and Forms 3.7 Facade Treatment and Double-Skin 3.8 Related Sustainable Strategies 3.9 Predictive Performance and Modeling 3.10 Fire Engineering/Smoke Control 3.11 Other Risks, Limitations and Challenges 3.12 Looking to the Future: Naturally Ventilating the Supertall 3.13 Conclusion: Challenging Industry and Occupant Preconceptions 4.0 Recommendations and Future Research 4.1 Recommendations 4.2 Future Research 5.0 References
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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