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Record W4413087783 · doi:10.1142/s1793042126500041

An improved lower bound on the image of the 2-adic character map for the Heisenberg algebra via modular linear differential equations

2025· article· en· W4413087783 on OpenAlexaff
Daniel Barake, Cameron Franc

Bibliographic record

VenueInternational Journal of Number Theory · 2025
Typearticle
Languageen
FieldMathematics
Topicadvanced mathematical theories
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMathematicsCharacter (mathematics)Algebra over a fieldModular designHeisenberg groupPure mathematicsImage (mathematics)Differential (mechanical device)GeometryPhysicsComputer science

Abstract

fetched live from OpenAlex

We describe families of MLDEs whose solutions are modular forms of level one that converge, [Formula: see text]-adically, to a Hauptmodul on [Formula: see text] by using a theorem of Serre. Then, we apply this to show that the image of the character map on the [Formula: see text]-adic Heisenberg VOA [Formula: see text] contains the space of [Formula: see text]-adic overconvergent modular forms [Formula: see text] of weight zero.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0010.003
Scholarly communication0.0040.007
Open science0.0030.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.021
GPT teacher head0.344
Teacher spread0.323 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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