Bayesian inference of thermal comfort: evaluating the effect of “well-being” on perceived thermal comfort in open plan offices
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
Abstract The judgment of thermal comfort is a cognitive process which is influenced by physical, psychological and other factors. Prior studies have shown that occupants, who are generally satisfied with many non-thermal conditions of indoor environmental quality, are more likely to be satisfied with thermal conditions as well. This paper presents a novel approach that considers the effect of non-thermal building environmental design conditions, such as indoor air quality and noise levels, on perceived thermal comfort in open-plan offices. The methodology involves the use of Bayesian inference to relate the occupant’s thermal dissatisfaction in a building not only to thermal conditions and occupant metabolic factors (i.e., parameters of the original Fanger model), but also to measurable non-thermal metrics of indoor environmental quality. A Bayesian logistic regression approach is presented in this paper. The experimental context regards a prior indoor environmental quality measurement and evaluation study of 779 occupants of open-plan offices throughout Canada and the US. We present revised PMV-PPD curves for real-world offices that take into account both thermal and wellbeing IEQ parameters. The Bayesian inference analysis reveals that the occupant’s thermal dissatisfaction is influenced by many non-thermal IEQ conditions, such as indoor CO 2 concentrations and the satisfaction with the office lighting intensity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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