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Record W2044288494 · doi:10.1063/1.4865560

One-magnon and exciton inelastic light scattering in the antiferromagnet CoF2

2014· article· en· W2044288494 on OpenAlexaff
E. Meloche, M. G. Cottam, D. J. Lockwood

Bibliographic record

VenueLow Temperature Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsInstitute for Microstructural SciencesWestern University
Fundersnot available
KeywordsMagnonX-ray Raman scatteringCondensed matter physicsSpin waveRaman scatteringPhysicsAntiferromagnetismInelastic neutron scatteringRaman spectroscopyExcitonScatteringLight scatteringAnisotropyInelastic scatteringOpticsFerromagnetism

Abstract

fetched live from OpenAlex

Experimental data are reported for the temperature and polarization dependence of the one-magnon Raman light scattering in the rutile-structure antiferromagnet CoF2 (Neel temperature TN = 38 K). The low-lying excitons are also investigated at low temperatures and comparisons made with results from earlier Raman, infrared, and neutron scattering work. A detailed analysis of the one-magnon Stokes and anti-Stokes Raman spectra is presented resulting in comprehensive data for the temperature variation up to TN of the one-magnon frequency, line width, and integrated intensity. A theory of the one-magnon scattering and other magnetic transitions is constructed based mainly on a spin S = 3/2 exchange model, extending a simpler effective S = 1/2 approach. The excitation energies and spectral intensities over a broad range of temperatures are obtained using a Green's function equation of motion method that allows for a careful treatment of the single-ion anisotropy. Overall the S = 3/2 theory compares well with the experimental data.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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