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Record W2167159301 · doi:10.1177/0040517510395994

Characterizing the performance of a single-layer fabric system through a heat and mass transfer model - Part II: Thermal and evaporative resistances

2011· article· en· W2167159301 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTextile Research Journal · 2011
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceThermalAir layerThermal resistanceAir gap (plumbing)Work (physics)Heat transferComposite materialEvaporative coolerMass transferLayer (electronics)ConvectionPorosityMechanicsNatural convectionMechanical engineeringThermodynamicsEngineering

Abstract

fetched live from OpenAlex

In Part I of this work, a heat and mass transfer model was developed to calculate the thermal and evaporative resistances of a single-layer fabric system. Using this model, the effects of environmental conditions, air gap, and material properties on the thermal and evaporative resistances have now been studied. The thickness of the air gap and that of the fabric layer were shown to contribute significantly to both the thermal resistance and evaporative resistance. The occurrence of natural convection in the air gap can cause decreases in thermal and evaporative resistances, and needs to be considered to determine the optimal air gap thickness. The porosity of the fabric layer has a distinct effect on the two resistances, and is an excellent property to help achieve both thermal protection and comfort. This work provides the fundamental basis for the optimization of garment fit and material properties to achieve good performance of the clothing system.

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.

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)0.004
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.214
GPT teacher head0.343
Teacher spread0.129 · 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