Thermal Comfort of Older People: Validation of the MPMV Model
Bibliographic record
Abstract
Older people are the most vulnerable to extreme heat and cold events, and understanding their thermal comfort requirements is an important component for the design of healthy buildings. There are, however, very few predictive thermal comfort models for older populations. The aim of this paper was to validate the newly developed MPMV model for older people using thermal sensation data collected in climatic chambers and field studies in urban and rural buildings under various climate zones. Besides the six customary physical parameters governing thermal perception, the model accounts for additional factors covering heat retrieval from or heat addition to the body core and adjustment constants for regulatory sweating rate and non-shivering thermogenesis, which are important for the thermal adaptation of people in real settings. The model predictions show good agreement with measurement in climate chambers, with an overall RMSE = 0.44. Similarly, the model reproduces field measurement with a high degree of accuracy in 71% of the studies, with RMSE = 0.52. The major differences were observed in urban and rural residences during the winter of cold climates and summer of hot climates. These discrepancies could be attributed to unreported factors, such as the transient effects, misestimation of personal input data, and seasonal adaptation of residents.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".