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Record W2141972423 · doi:10.1177/0040517512461707

Characterization of textile fabrics under various thermal exposures

2012· article· en· W2141972423 on OpenAlexafffund
Sumit Mandal, Guowen Song, M. Y. Ackerman, Stephen A. Paskaluk, Farzan Gholamreza

Bibliographic record

VenueTextile Research Journal · 2012
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsTextileEmissivityThermal protectionMaterials scienceComposite materialThermalThermal resistanceThermal comfortEnvironmental scienceMeteorology

Abstract

fetched live from OpenAlex

This study explores the behavior of textile fabrics under thermal exposures. The performance of thermal protective textile fabric systems with different structural features was evaluated under laboratory simulated thermal exposures. The study demonstrated that the protective performance of textile fabric systems varies with different types of thermal exposure. To provide effective protection in flame and radiant-heat exposures, the most important fabric properties to address are emissivity, absorptivity and thermal resistance. In hot surface exposures, the compression property of the fabric systems is the primary feature to consider for protection. Hot water and steam exposures produce mass transfer through fabrics. In the presence of water or steam jet pressure, fabric compression is a primary factor in protecting the human body. The findings obtained in this study can be used to engineer fabric systems that provide better protection from various thermal exposures.

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)0.001
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
Metaresearch0.0000.001
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)0.0010.000

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 head0.114
GPT teacher head0.384
Teacher spread0.270 · 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

Citations105
Published2012
Admission routes2
Has abstractyes

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