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Record W4410461439 · doi:10.1016/j.enbuild.2025.115888

Modeling heat loss through sweating: Towards improved heat load prediction from child occupants

2025· article· en· W4410461439 on OpenAlexafffund
Farah Youssef, Stéphane Hallé, Katherine D’Avignon

Bibliographic record

VenueEnergy and Buildings · 2025
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeat loadEnvironmental scienceEngineeringThermodynamics

Abstract

fetched live from OpenAlex

The design of HVAC systems requires correct estimation of both sensible and latent heat gains from occupants in order to assess cooling loads. Children differ significantly from adults in their heat production and thermoregulation in part due to variations in body mass, height, and sweat gland development, resulting in a unique sensible heat ratio for children. Though in spaces such as school classrooms and gymnasiums or daycares, children are the dominant occupant group, they are absent from load tables documenting the rate of net heat production by occupants and their sensible heat ratio. This study presents a steady-state, child-specific heat balance model, developed by modifying existing first-order thermodynamic equations from the literature to account for children’s unique anthropometric characteristics. Through a comprehensive search process, experimental data on children’s heat loss through sweating was gathered and analyzed. Statistical analysis of this data revealed the shortcomings of adult-based models in calculating the rate of evaporative heat loss through sweating per body surface area of children, even in mild environmental conditions. A novel regression model of the evaporative heat loss through sweating as a function of the air dry bulb temperature and net heat generation is proposed. This research lays the groundwork for the creation of comprehensive child-specific heat load tables, essential for optimizing HVAC systems in spaces designed for children.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.278
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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