Assessment of Vernacular Housing in the Dominican Republic Using Simulations
Bibliographic record
Abstract
Dominican vernacular architecture, based on the Spanish-influenced indigenous bohio, is built with natural materials. This housing model has remained practically unchanged for five centuries, responding adequately to the tropical Caribbean climate. However, it is necessary to characterize this behavior to verify the indoor comfort conditions of this housing typology. The aim of this research is to evaluate the behavior of a vernacular house located in Villa Sombrero, Peravia Province, Dominican Republic, using a simulation model. For this purpose, a bohio was selected, which has a simple rectangular volume. Simulations were carried out using Design Builder software, considering the passive strategies incorporated in the bohio. The results indicate that indoor ambient temperature remains within the comfort range throughout the annual cycle. It was confirmed that the highest solar gain through exterior windows occurs in the warmer months. Lighting consumption varies between approximately 195 kWh and 220 kWh, with a more stable behavior during the middle months of the year. CO2 emissions followed the same behavior as lighting consumption since it was the only energized element in the bohio. This research demonstrated that passive strategies implemented in the vernacular dwelling work correctly, except for the minimum illuminance level, which needs to be improved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".