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Record W2904732373 · doi:10.1063/1.5055899

Note: Carbon fiber composite arrow shaft as cryogenic structural support material

2018· article· en· W2904732373 on OpenAlexafffund
Clifford E. Plesha, E. M. Tonita, J. B. Kycia

Bibliographic record

VenueReview of Scientific Instruments · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceComposite numberComposite materialCryogenicsCarbon fibersFiberCarbon fiber compositePhysicsThermodynamics

Abstract

fetched live from OpenAlex

The thermal conductivity, electrical resistivity, and bending stiffness of a carbon fiber archery arrow shaft were measured in order to determine if it would be a good material to use as a structural support in a cryogenic environment. It shows promise because of its thin cross section and structural rigidity. The thermal conductivity of the material was measured from 0.1 K to 1 K to be κ(T)=8.8 × 10−5 T1.54Wcm K, which is on the order of other thermal insulating materials used at cryogenic temperatures. The electrical resistivity is 0.044 Ω cm at 0.2 K, and the bending stiffness is 243 N m2 at room temperature. The shafts were machined to reduce overall thermal conductance. The reduction in thermal resistivity was calculated, and the change in stiffness was measured after shafts were machined. The shafts were used to form a support structure for an adiabatic demagnetization refrigerator. The heat load was then calculated. The carbon fiber arrow shaft provides an outstanding, thermal insulating support structure.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.091
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0290.003

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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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; both teacher heads agree on what is shown here.

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 routes2
Has abstractyes

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