Modelling skin temperature of a human exercising in an outdoor environment
Bibliographic record
Abstract
Skin temperature monitoring is an important component when estimating thermoregulatory responses due to heat exchange at the skin surface.The aim of this study is to improve the accuracy of mean skin temperature ( Tsk ) predictions in human thermal comfort models, specifically the COMFA (COMfort FormulA) outdoor model, in order to reduce errors in energy budget estimates associated with Tsk .Field tests were conducted on 12 subjects performing 30 minutes of steadystate physical activity (running or cycling) on two separate occasions.The predicted thermal sensations (PTS) from the COMFA budget model using both actual (measured) and predicted (with model) Tsk were compared at 5-minute intervals.Results indicate that the model over-predicted Tsk throughout the exercise period.The root mean square error (RMSE) of Tsk of subjects running was larger than cycling, and increased throughout the 30 minute exercise session; hence, the model was less able to accurately predict Tsk as metabolic activity increased.The Spearman's correlation coefficients (r s ) for actual thermal sensation (ATS) with both actual and predicted Tsk thermal sensation scores were low, (r s =0.315 and 0.285, respectively).However, ATS votes correlated more strongly with predicted thermal sensation (PTS) scores in running tests.Added psychological and physiological variables when exercising outdoors make it inherently difficult to apply current thermal comfort (TC) models to exercising subjects.There is need for further studies regarding prediction of overall TC while exercising outdoors for use in urban design and planning, as well as adapting models to specific types of exercise.
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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 | 0.000 | 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) | 0.001 | 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".