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Record W3158603062 · doi:10.1103/physrevb.105.125118

Crystallography of hyperbolic lattices

2022· article· en· W3158603062 on OpenAlexafffund
Igor Boettcher, Alexey V. Gorshkov, Alicia J. Kollár, Joseph Maciejko, Steven Rayan, Ronny Thomale

Bibliographic record

VenuePhysical review. B./Physical review. B · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicBlack Holes and Theoretical Physics
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
FundersCanada Research ChairsDeutsche ForschungsgemeinschaftUniversity of AlbertaMultidisciplinary University Research InitiativeCanadian Institute for Advanced ResearchBoettcher FoundationAdvanced Scientific Computing ResearchGovernment of AlbertaU.S. Department of EnergyArmy Research OfficeNatural Sciences and Engineering Research Council of CanadaAir Force Office of Scientific ResearchDivision of Mathematical SciencesNational Science Foundation
KeywordsMathematicsCrystallographyPure mathematicsChemistry

Abstract

fetched live from OpenAlex

Hyperbolic lattices are intricately wired networks that constitute an exciting experimental platform for tabletop simulations of physical models in synthetic negatively curved spaces. This work reveals a hidden crystal order underneath the labyrinthine beauty of such lattices - an order made of unit cells arranged periodically onto the scaffold of a hyperbolic Bravais lattice. This sets the stage for applying powerful techniques from crystallography and band theory to describe experiments in circuit quantum electrodynamics and topoelectrical circuit networks as well as to search for novel phases of matter.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.331
Teacher spread0.320 · 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

Citations116
Published2022
Admission routes2
Has abstractyes

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