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Record W1976392972 · doi:10.1177/004051750007000214

Reducing Wind-Induced Heat Loss Through Multilayer Clothing Systems by Means of a Bypass Layer

2000· article· en· W1976392972 on OpenAlexafffund
R. J. Kind, Cabot Broughton

Bibliographic record

VenueTextile Research Journal · 2000
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsCarleton University
FundersMinistère de la Défense Nationale
KeywordsLayer (electronics)Wind tunnelAirflowAir layerClothingMaterials scienceFlow resistanceFlow (mathematics)Composite materialMechanicsMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper concerns multilayer clothing systems whose outermost layers consist of a somewhat permeable exterior sheath and a layer of light insulating batting. Wind-tunnel tests show that the wind-induced heat loss through such systems can be greatly reduced by introducing a layer with low resistance to airflow between the sheath and the underlying batting layer. The low resistance layer acts as a bypass, allowing air that has penetrated the somewhat permeable sheath to flow downstream with little or no penetra tion of the batting layer. The results have been correlated into a form suitable for guiding of design decisions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
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 head0.132
GPT teacher head0.406
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2000
Admission routes2
Has abstractyes

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