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Record W2748489276 · doi:10.1063/1.5000101

Kinetic energy and momentum distribution of isotopic liquid helium mixtures

2017· article· en· W2748489276 on OpenAlexafffund
Massimo Boninsegni

Bibliographic record

VenueThe Journal of Chemical Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKinetic energyHeliumMomentum (technical analysis)Distribution (mathematics)ThermodynamicsNuclear physicsPhysicsMaterials scienceAtomic physicsClassical mechanics

Abstract

fetched live from OpenAlex

The momentum distribution and atomic kinetic energy of the two isotopes of helium in a liquid mixture at temperature T = 2 K are computed by quantum Monte Carlo simulations. Quantum statistics is fully included for 4He, whereas 3He atoms are treated as distinguishable. Comparison of theoretical estimates with a collection of the most recent experimental measurements shows reasonable agreement for the energetics of 4He and pure 3He. On the other hand, a significant discrepancy (already observed in previous studies) is reported between computed and measured values of the 3He kinetic energy in the mixture, especially in the limit of low 3He concentration. We assess quantitatively the importance of Fermi statistics and find it to be negligible for a 3He concentration ≲20%. Our results for the momentum distribution lend support to what was already hypothesized by other authors, namely, that the discrepancy is likely due to underestimation of the 3He kinetic energy contribution associated with the tail of the experimentally measured momentum distribution.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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.

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

Citations14
Published2017
Admission routes2
Has abstractyes

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