A Learning Machine Approach for Predicting Thermal Comfort Indices
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
Human thermal comfort is influenced by psychological as well as physiological factors. Several comfort indices, such as PMV, PPD, TSENS, ET*, DISC, and SET* (see nomenclature) have been developed. These indices attempt to correlate human thermal comfort with environmental conditions. This paper describes the use of a learning algorithm “support vector machine (SVM) learning” for prediction of the thermal comfort indices. The SVM is an artificial intelligent approach that can capture the input/output mapping from the given data. Support vector machines were developed based on the Structural Risk Minimization principle. Different sets of representative experimental environmental factors that affect a homogenous person’s thermal balance were used for training the SVM algorithm. The results demonstrate good correlation between SVM predicted values and those obtained from conventional thermal comfort, such as Fanger Model and “2-Node” model. The “trained SVM” with representative data could be easily and more effectively used to predict the indices compared to other conventional estimation methods.
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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 | 0.000 | 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 it