Prediction of the thermal comfort indices using improved support vector machine classifiers and nonlinear kernel functions
Why this work is in the frame
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Bibliographic record
Abstract
A new prediction method for thermal comfort indices is introduced. This method of prediction titled ‘support vector machine (SVM)’ uses learning as a process to emulate human intelligence. In this paper, more adequate nonlinear kernels have been used and the SVM has been improved to predict the thermal comfort indices accurately. In this study, we focus mainly on supervised learning machine where an instructor provides the output samples during the learning phase. Different sets of representative experimental factors, such as air temperature, mean radiant temperature, relative humidity, air velocity, metabolism and clothing value that affect a person’s thermal balance were used for training the SVM machine. The results show the best correlation between SVM predicted values with a polynomial kernel of the second order and those obtained from conventional thermal comfort, such as the Fanger model and the ‘2-Node’ model.
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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