MétaCan
Menu
Back to cohort
Record W4387983302 · doi:10.21468/scipost.report.7440

Report on scipost_202305_00044v1

2023· peer-review· en· W4387983302 on OpenAlexafffund
Abhay Kumar Nayak, Jonathan Reiner, Hengxin Tan, Huixia Fu, Henry Ling, Chandra Shekhar, Claudia Felser, T. Pereg-Barnea, Binghai Yan, Haim Beidenkopf, Nurit Avraham

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaGerman-Israeli Foundation for Scientific Research and DevelopmentIsrael Science FoundationWeizmann Institute of Science
KeywordsGeography

Abstract

fetched live from OpenAlex

Strong singularities in the electronic density of states amplify correlation effects and play a key role in determining the ordering instabilities in various materials.Recently high order van Hove singularities (VHSs) with diverging power-law scaling have been classified in single-band electron models.We show that the 110 surface of Bismuth exhibits high order VHS with unusually high exponent of -0.7.Detailed mapping of the surface band structure using scanning tunneling microscopy and spectroscopy shows that this singularity occurs in close proximity with Dirac bands located at the center of the surface Brillouin zone.The enhanced power-law divergence is shown to originate from the anisotropic flattening of the Dirac band just above the Dirac node.Such near-coexistence of massless Dirac electrons and ultra-massive saddle points enables to study the interplay of high order VHS and Dirac fermions.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.081
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.9190.864

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 head0.048
GPT teacher head0.332
Teacher spread0.284 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same topicTopological Materials and PhenomenaFrench-language works237,207