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Record W3007861565 · doi:10.1520/jte20180238

Thermal Resistance of Air-Filled Mattresses: Measurement Repeatability and the Effect of Selected Test Parameters

2018· article· en· W3007861565 on OpenAlexaff
Shelley Kemp, Xiaoan Shen, René M. Rossi, Martin Camenzind

Bibliographic record

VenueJournal of Testing and Evaluation · 2018
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsMountain Equipment Co-op (Canada)
Fundersnot available
KeywordsRepeatabilityMaterials scienceComposite materialTest methodStructural engineeringMathematicsStatisticsEngineering

Abstract

fetched live from OpenAlex

Abstract There is no standardized test method specifically for determining the thermal resistance (R-value) of air-filled mattresses. Unacceptable inter- and intra-laboratory variations in round-robin testing have been attributed to differences in the test apparatus and test parameters used. To identify relevant sources of variation, the repeatability of the guarded hotplate apparatus (in a double plate configuration) was first characterized, and then the effect of modifying selected test parameters on mattress thickness and thermal resistance was investigated. Two mattress types, in two different sizes, were examined: one contained air only while the other contained air plus a nonwoven polyester fill. It was found that repeatable outcomes could be attained when using the guarded hotplate apparatus (95 % repeatability limit of less than 0.08 m2K/W for all mattresses tested). The modification of test parameters had significant effects on mattress thickness or R-value, or both. External pressure, the temperature difference across the specimen, supplementary insulation, mattress size, and environmental conditions affected both the thickness and R-value of the test mattresses. Inflation pressure, over the range tested, did not have a significant effect on the R-value but did influence mattress thickness. This work highlights the need for the standardization of the test apparatus and test parameters and will aid in the development of a standardized test method.

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 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)0.015
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.324
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.021
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)0.0000.000

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 head0.050
GPT teacher head0.299
Teacher spread0.250 · 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 teacher head, not a consensus.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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