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Record W4281705150 · doi:10.1088/1361-648x/ac7629

Intermediate valence state in YbB<sub>4</sub> revealed by resonant x-ray emission spectroscopy

2022· article· lv· W4281705150 on OpenAlexafffund
Felix Frontini, Blair W. Lebert, K.K. Cho, M S Song, B K Cho, Christopher J. Pollock, Young‐June Kim

Bibliographic record

VenueJournal of Physics Condensed Matter · 2022
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicRare-earth and actinide compounds
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of KoreaCanada Foundation for InnovationNational Science Foundation
KeywordsValence (chemistry)SpectroscopyFrustrationKondo effectCondensed matter physicsChemistryLattice (music)Atomic physicsPhysicsElectrical resistivity and conductivityQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract We report the temperature dependence of the Yb valence in the geometrically frustrated compound Y b B 4 from 12 to 300 K using resonant x-ray emission spectroscopy at the Yb L α 1 transition. We find that the Yb valence, v, is hybridized between the v = 2 and v = 3 valence states, increasing from v = 2.61 ± 0.01 at 12 K to v = 2.67 ± 0.01 at 300 K, confirming that Y b B 4 is a Kondo system in the intermediate valence regime. This result indicates that the Kondo interaction in Y b B 4 is substantial, and is likely to be the reason why Y b B 4 does not order magnetically at low temperature, rather than this being an effect of geometric frustration. Furthermore, the zero-point valence of the system is extracted from our data and compared with other Kondo lattice systems. The zero-point valence seems to be weakly dependent on the Kondo temperature scale, but not on the valence change temperature scale T v .

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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.227
Teacher spread0.220 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

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