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Record W2669819778 · doi:10.1103/physrevc.96.054007

Quantum Monte Carlo calculations of light nuclei with local chiral two- and three-nucleon interactions

2017· article· en· W2669819778 on OpenAlexafffund
J. E. Lynn, Ingo Tews, J. Carlson, Stefano Gandolfi, Alexandros Gezerlis, K. E. Schmidt, A. Schwenk

Bibliographic record

VenuePhysical review. C · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsUniversity of Guelph
FundersLos Alamos National LaboratoryNatural Sciences and Engineering Research Council of CanadaJoint Institute for Nuclear Astrophysics - Center for the Evolution of the ElementsNational Energy Research Scientific Computing CenterTechnische Universität DarmstadtLaboratory Directed Research and DevelopmentU.S. Department of EnergyEuropean Research CouncilNational Science Foundation
KeywordsPhysicsMonte Carlo methodQuantum Monte CarloNucleonStatistical physicsQuantumNuclear physicsQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

The authors present a detailed report on quantum Monte Carlo (QMC) calculations of light nuclei with local two- and three-body interactions from chiral effective field theory. They establish QMC methods with local chiral interactions as a versatile and systematic approach to $a\phantom{\rule{0}{0ex}}b-i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$ calculations of light nuclei.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.367
Teacher spread0.342 · 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 designSimulation or modeling
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

Citations92
Published2017
Admission routes2
Has abstractyes

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