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Record W1997095373 · doi:10.1103/physrevb.71.144406

Critical slowing down in the two-dimensional Ising model measured using ferromagnetic ultrathin films

2005· article· en· W1997095373 on OpenAlexaff
M. J. Dunlavy, D. Venus

Bibliographic record

VenuePhysical Review B · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCondensed matter physicsFerromagnetismCritical exponentScalingPhysicsIsing modelCurie temperatureExponentMagnetizationPower lawMaterials scienceQuantum mechanicsMagnetic fieldPhase transitionStatisticsMathematics

Abstract

fetched live from OpenAlex

Power-law scaling of the relaxation time $\ensuremath{\tau}$ associated with critical slowing down has been experimentally measured in the dynamics of the magnetization of a bilayer of iron grown on top of a $W(110)$ substrate using the complex magnetic ac susceptibility $\ensuremath{\chi}(T)$. The observed value of the critical exponent for the slowing down above the Curie transition of this two-dimensional Ising ferromagnetic system is $z\ensuremath{\nu}=2.09\ifmmode\pm\else\textpm\fi{}0.06$ (95% confidence), in agreement with most contemporary theories and simulations. Further analysis reveals that dynamical effects cause $\ensuremath{\chi}(T)$ to deviate from power-law scaling as the temperature is decreased towards ${T}_{c}$, whereas the saturation of the correlation length due to finite-size effects (on the order of 500 lattice spaces) limits the divergence of $\ensuremath{\tau}$.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.028
GPT teacher head0.331
Teacher spread0.304 · 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

Citations33
Published2005
Admission routes1
Has abstractyes

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