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Record W3029032344 · doi:10.1007/jhep10(2019)010

3d modularity

2019· article· en· W3029032344 on OpenAlexafffund
Miranda C. N. Cheng, Sungbong Chun, Francesca Ferrari, Sergei Gukov, Sarah M. Harrison

Bibliographic record

VenueJournal of High Energy Physics · 2019
Typearticle
Languageen
FieldMathematics
TopicAlgebraic structures and combinatorial models
Canadian institutionsMcGill University
FundersHigh Energy PhysicsFonds de recherche du Québec – Nature et technologiesHorizon 2020 Framework ProgrammeInternational Laboratory of Mirror Symmetry and Automorphic Forms, National Research University Higher School of EconomicsStrongMinistero dell’Istruzione, dell’Università e della RicercaOffice of ScienceSamsungCanada Research ChairsU.S. Department of EnergyWalter Burke Institute for Theoretical PhysicsNational Science Foundation
KeywordsModularity (biology)Modular designPure mathematicsHomogeneous spaceMathematicsTensor productTensor (intrinsic definition)Variety (cybernetics)Modular formAlgebra over a fieldTopology (electrical circuits)Computer scienceCombinatoricsGeometry

Abstract

fetched live from OpenAlex

A bstract We find and propose an explanation for a large variety of modularity-related symmetries in problems of 3-manifold topology and physics of 3d $$ \mathcal{N} $$ N = 2 theories where such structures a priori are not manifest. These modular structures include: mock modular forms, SL(2 , ℤ) Weil representations, quantum modular forms, non-semisimple modular tensor categories, and chiral algebras of logarithmic CFTs.

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: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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

Citations70
Published2019
Admission routes2
Has abstractyes

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