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Helium Mass Flow Through a Solid-Superfluid-Solid Junction

2015· article· en· W2312562966 on OpenAlexafffund
Zhi Gang Cheng, John Beamish, Andrew Fefferman, Fabien Souris, S. Balibar, V. Dauvois

Bibliographic record

VenuePhysical Review Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSuperfluidityMaterials scienceMass transferFlow (mathematics)Isotopes of heliumImpuritySuperfluid helium-4Condensed matter physicsHeliumMass flowHelium-4Solid solutionMechanicsThermodynamicsPhysicsAtomic physics

Abstract

fetched live from OpenAlex

We report the results of flow experiments in which two chambers containing solid ^{4}He are connected by a superfluid Vycor channel. At low temperatures and pressures, mechanically squeezing the solid in one chamber produced a pressure increase in the second chamber, a measure of mass transport through our solid-superfluid-solid junction. This pressure response is very similar to the flow seen in recent experiments at the University of Massachusetts: it began around 600 mK, increased as the temperature was reduced, then decreased dramatically at a temperature, T_{d}, which depended on the ^{3}He impurity concentration. Our experiments indicate that the flow is limited by mass transfer across the solid-liquid interface near the Vycor ends, where the ^{3}He collects at low temperature, rather than by flow paths within the solid ^{4}He.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.309
Teacher spread0.285 · 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

Citations41
Published2015
Admission routes2
Has abstractyes

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