Natural ventilation in high-rise office buildings : an output of the CTBUH Sustainability Working Group : CTBUH technical guide
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
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.023 |
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".