Considering child-specific view factors in human thermal balance
Bibliographic record
Abstract
View factors VF in human heat balance calculations are influenced by shape and size, yet existing standards use uniform values for all. Previous research has examined height and weight variations within adult populations, but no studies have investigated children’s view factors. To address this, we created a numerical manikin representing an average 5-year-old boy in the standing posture and one based on the average male participant from Fanger, using a human shape generator. We calculated projected area factors fp for both numerical manikins using the parallel ray method, calculated VF and created graphical representations as a function of wall dimensions and distances. Our analysis showed significant differences in fp between the adult and 5-year-old child, with variations up to 22%, significantly higher than those found between adults in the literature. Using adult and child VF in a test scenario, the 5-year-old child’s mean radiant temperature MRT was ∼1 °C higher than the adult’s with radiant floor heating, and ∼0.6 °C higher with a chilled ceiling. This significant MRT difference demonstrates that child-specific view factors should be considered in future thermal comfort calculations for young children.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".