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Record W2023203227 · doi:10.1103/physrevb.73.172416

Aging and memory effects in zero-field-cooled collections of two-level subsystems

2006· article· en· W2023203227 on OpenAlexaff
C. A. Viddal, R. M. Roshko

Bibliographic record

VenuePhysical Review B · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsZero (linguistics)Moment (physics)Field (mathematics)PhysicsCondensed matter physicsFunction (biology)Constant (computer programming)Anomaly (physics)Zero temperatureSecond moment of areaMagnetic momentSpin (aerodynamics)Atomic physicsThermodynamicsQuantum mechanicsMathematicsComputer science

Abstract

fetched live from OpenAlex

Numerical simulations of the temperature and time dependence of the moment of a collection of thermally activated, interacting two-level subsystems, explicitly prepared under zero-field-cooled (ZFC) conditions, are presented, which clearly demonstrate that such systems do indeed exhibit aging and memory effects analogous to those observed in collectively frozen spin glasses. In particular, if zero-field cooling at a constant cooling rate is interrupted temporarily by aging in zero field at a constant temperature ${T}_{a}$ for a time $\ensuremath{\Delta}{t}_{a}$ before the system is subsequently probed by the application of a magnetic field, then the aged moment, measured either as a function of temperature $T$ on warming at a fixed rate, or as a function of observation time $\mathrm{ln}\phantom{\rule{0.2em}{0ex}}t$ at fixed temperature ${T}_{a}$, will lie below the unaged moment, and the difference between the aged and unaged response functions will exhibit an anomaly in the vicinity of either the aging temperature or the aging time, depending on the experiment which is performed.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · 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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.007
GPT teacher head0.269
Teacher spread0.262 · 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 designTheoretical or conceptual
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

Citations3
Published2006
Admission routes1
Has abstractyes

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Same venuePhysical Review BSame topicTheoretical and Computational PhysicsFrench-language works237,207