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Compression-Driven Mass Flow in Bulk Solid<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mmultiscripts><mml:mrow><mml:mi>He</mml:mi></mml:mrow><mml:mprescripts/><mml:none/><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:mmultiscripts></mml:mrow></mml:math>

2016· article· lv· W2465227105 on OpenAlexafffund
Zhi Gang Cheng, John Beamish

Bibliographic record

VenuePhysical Review Letters · 2016
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlow (mathematics)Materials scienceMass flowSaturation (graph theory)SuperfluidityAnalytical Chemistry (journal)PhysicsThermodynamicsCondensed matter physicsMechanicsChemistryChromatographyMathematicsCombinatorics

Abstract

fetched live from OpenAlex

Mass flow has been observed in solid ^{4}He coexisting with superfluid confined in Vycor, but its physical mechanism remains an open question. Here we report observations of flow in experiments in which Vycor has been eliminated, allowing us to study the intrinsic flow in solid ^{4}He without the complications introduced by the presence of superfluid and the associated solid-liquid interfaces. By growing crystals with ^{3}He concentration as low as x_{3}=5×10^{-12}, we also avoided the low temperature flow suppression observed in previous experiments and found that the flow rate continued to increase down to at least 28 mK without saturation. In addition, ^{3}He concentrations of 120 ppb, which suppressed most of the low temperature flow in previous experiments, had no effect in our samples. The larger ^{3}He concentrations needed to block the bulk solid flow suggest that the mass flow involves a larger area, such as disordered liquid layer on solid surface and grain boundaries.

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 score0.002
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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.256
Teacher spread0.238 · 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

Citations36
Published2016
Admission routes2
Has abstractyes

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