Enhancing Thermal Comfort in High-Rise Condominiums Through Passive Design Strategies in Lagos, Nigeria
Bibliographic record
Abstract
Thermal comfort in buildings is a challenging environmental issue confronting occupants, especially in tropical climates like Nigeria.Thermal comfort is essential in urban centres like Lagos due to high population density, limited urban land, and congestion.The impacts manifest more in compact high-rise buildings not adopting passive design strategies.The study investigates the passive design strategies adopted in high-rise condominiums designed to enhance occupants' thermal comfort in Lagos, Nigeria.The study relied on qualitative data collected through case studies, observation checklists, and in-depth interview guides from seven purposely selected high-rise condominiums.Data were analysed using simple statistical tools and content analysis techniques.The data analysis showed that only 28.6% (2 out of 7 case studies) of the buildings adopted passive design strategies.71.4% (5 out of 7 case studies) depended on active ventilation for thermal comfort.Findings show that passive design strategies in high-rise condominium buildings consider a location's climate and site conditions to maximise the health and comfort of building occupants while minimising energy use.It is relevant and is in tandem with SDGs 3, 11and 13.The results implied that occupants are experiencing discomfort with thermal indoor qualities in their homes.The study concludes that thermal discomfort affects occupants' well-being.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".