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Record W2162605514 · doi:10.1177/0040517513485625

Characterizing factors affecting the hot liquid penetration performance of fabrics for protective clothing

2013· article· en· W2162605514 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 · 2013
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPenetration (warfare)Materials scienceComposite materialClothingMoistureThermalEngineering

Abstract

fetched live from OpenAlex

Hot liquid hazards present in work environments are well known to be a considerable risk in workplace safety for numerous industries. In this work, the effects of different liquids and temperatures on penetration performance of fabrics were investigated, and the influence of impingement angle on protective performance of liquid penetration was also studied. Several kinds of fabrics for protective clothing were used to characterize the penetration behaviors of protective materials. The results showed the liquid temperature had a significant impact on the stored and penetrated amount of liquids. Different liquids can lead to distinct damage to fabrics. The impingement angle affects liquid transfer (storage and penetration) through the fabric. The addition of a thermal liner or moisture barrier can sharply decrease the penetration. The results provide new insights into the development of functional garments/materials and better methods for evaluating the performance of these materials under hazardous work environments.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.119
GPT teacher head0.375
Teacher spread0.257 · 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