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Record W2113772296 · doi:10.1126/science.1177112

Itinerant Ferromagnetism in a Fermi Gas of Ultracold Atoms

2009· article· en· W2113772296 on OpenAlexaff
Gyu-Boong Jo, Ye-Ryoung Lee, Jaehoon Choi, Caleb A. Christensen, Tony H. Kim, Joseph H. Thywissen, David E. Pritchard, Wolfgang Ketterle

Bibliographic record

VenueScience · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFerromagnetismCondensed matter physicsFermionDelocalized electronFermi gasPhysicsFermi Gamma-ray Space TelescopeLattice (music)Ultracold atomSpin (aerodynamics)Quantum mechanicsElectronQuantum

Abstract

fetched live from OpenAlex

Cold Atom Magnetism Magnetic ordering arises from the strong interactions between atoms, with its origins deeply rooted in quantum mechanics. How the ordering comes about, however, has long been a topic of debate because most condensed-matter systems are limited by a somewhat fixed parameter space. Cold atom systems, by comparison, provide the ability to tune the magnitude and sign of the atom-atom interaction, as well as the density. Jo et al. (p. 1521 ; see the Perspective by Zwerger ) exploit this flexibility to use an ensemble of ultracold fermionic atoms as a “quantum simulator” to explore the possibility of magnetic ordering. As the repulsive interaction between atoms is increased, an instability occurs in the free two-component Fermi gas (or jellium), which results in a phase transition and the ferromagnetic ordering of the atoms.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.247
Teacher spread0.241 · 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

Citations310
Published2009
Admission routes1
Has abstractyes

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