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Record W2521677900 · doi:10.1126/sciadv.1601086

Quasi-particle interference of heavy fermions in resonant x-ray scattering

2016· article· en· W2521677900 on OpenAlexafffund
András Gyenis, Eduardo H. da Silva Neto, Ronny Sutarto, E. Schierle, Feizhou He, E. Weschke, Mariam Kavai, Ryan Baumbach, J. D. Thompson, E. D. Bauer, Z. Fisk, A. Damascelli, Ali Yazdani, Pegor Aynajian

Bibliographic record

VenueScience Advances · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRare-earth and actinide compounds
Canadian institutionsCanadian Light Source (Canada)University of British ColumbiaCanadian Institute for Advanced Research
FundersDivision of Materials ResearchNational Research Council CanadaMaterials Research Science and Engineering Center, Harvard UniversityHelmholtz-Zentrum Berlin für Materialien und EnergieCanada Research ChairsCanada Foundation for InnovationUniversity of SaskatchewanBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaCanadian Light SourceCanadian Institute for Advanced ResearchKillam TrustsPrinceton Center for Complex MaterialsBinghamton UniversityGordon and Betty Moore FoundationGovernment of SaskatchewanWestern Economic Diversification CanadaW. M. Keck FoundationU.S. Department of EnergyDivision of Materials Sciences and EngineeringCanadian Institutes of Health ResearchNational Science Foundation
KeywordsScatteringInterference (communication)Particle (ecology)ElectronPhysicsQuantum tunnellingCondensed matter physicsMaterials scienceOpticsNuclear physicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

-electron bands in these compounds. The scattering enhancement is consistent with the measured quasi-particle interference signal in the STM measurements, indicating that the quasi-particle interference can be probed through the momentum distribution of RXS signals. Overall, our experiments demonstrate new opportunities for studies of correlated electronic systems using the RXS technique.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.495
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.288
Teacher spread0.271 · 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 teacher head, 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

Citations7
Published2016
Admission routes2
Has abstractyes

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