New considerations for representing moisture in indoor thermal conditions: Associations between enthalpy, cognitive performance, and thermal sensations
Bibliographic record
Abstract
Motivated by limitations with the use of temperature and thermal comfort models in relation to occupant health outcomes, this work investigates numerous characterizations of thermal conditions and associations among these thermal variables, cognitive performance, and thermal perceptions. Measurements of classroom dry-bulb temperature and relative humidity were used to calculate a suite of eleven thermal variables, which were paired with thermal sensation votes and cognitive test responses from graduate students attending classes in these monitored spaces, resulting in an analysis dataset of 273 observations from 54 participants. Results from Spearman Rank correlation coefficients, factor analysis, and principal component analysis suggest that the eleven thermal variables cluster into three groups that reflect variations in indoor temperature, indoor relative humidity, and indoor-outdoor differences. While several variables appear to reflect variations in only air temperature (e.g., PMV estimates) or moisture, indoor enthalpy appears to reflect variations in temperature and RH in the most balanced manner. A series of mixed effects statistical models suggest that higher values of indoor enthalpy appear to be associated with improved cognitive test scores and warm sensations, and warm sensations appear to be associated with improved cognitive test scores. The collective results posit new considerations for the importance of indoor moisture with respect to occupant outcomes and how commonly used modelling approaches may not reflect this. Additional research that incorporates diverse populations, varied built environments, and causal methods could help further our understanding of the effects of air temperature and moisture on occupant outcomes in varied built environment settings.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".