Exploration of the relationships between perceived and observed parameters of IEQ using Bayesian analysis
Bibliographic record
Abstract
Abstract Seeking to bridge the gap between observations and predictions of thermal comfort, recent work has explored novel predictive frameworks to improve the prediction accuracy of occupants’ thermal satisfaction in office spaces. Recent contributions include the development of a Bayesian framework to estimate the probability of an occupant feeling thermally satisfied as a function of not only psychrometric IEQ parameters but also non-thermal metrics of IEQ. A predictive relationship between indoor CO2 concentrations and thermal satisfaction was found, though the underlying causal relationship is not yet clear. An occupant unhappy about air quality is more likely than not found to be unhappy with other parameters, including indoor air temperature. To quantify these relationships, further analysis with new modelling methods and data is required. This paper presents a new formulation of prior work, using a new Bayesian logistic regression model and counterfactual inference to assess the combined relationships between many subjective and objective IEQ factors. This work sets out to provide the first-known Bayesian analysis of the underlying causality of observed statistical relationships between divergent parameters of subjective and objective IEQ.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".