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Record W4385637298 · doi:10.1177/00405175231189892

Optimization of the quilting method and filling quality of cold-proof down clothing based on thermal insulation performance

2023· article· en· W4385637298 on OpenAlex
Lifang Wang, Hongling Liu, Denis Rodrigue, Zhaoqun Du, Xiaodong Wang

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 · 2023
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversité Laval
FundersChina Scholarship Council
KeywordsQuiltingThermal conductivityMaterials scienceBar (unit)Thermal insulationThermal resistanceComposite materialClothingWork (physics)ThermalEngineering drawingMechanical engineeringEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

This work investigates the thermal insulation performances of clothing based on down and a quilting method. The effect of several parameters was studied, including the amount of down, quilting number, and their geometry. An experimental study was combined with a geometrical model to confirm that a regular hexagonal geometry is the best to maximize the heat insulation properties. For the overall tiling, the best thermal resistance was obtained by using 8.67 g of down, and the thermal conductivity is the lowest when the filling was 5.14 g down. The heat resistance was also found to increase by decreasing the quilting number, but the effect is less significant as the number increases. Also, a lower amount of down in each quilting place resulted in higher heat loss. So, improving the filling down quality helped to increase heat retention. The correct space division and filling quality lead to improved warmth retention of cold protection down products.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.174
GPT teacher head0.443
Teacher spread0.269 · 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