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Record W4399062549 · doi:10.1112/mtk.70067

One‐level densities in families of Grössencharakters associated to CM elliptic curves

2025· article· en· W4399062549 on OpenAlexafffund
Chantal David, Lucile Devin, Ezra Waxman

Bibliographic record

VenueMathematika · 2025
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Mathematical Modeling in Engineering
Canadian institutionsInternational Centre for Comparative CriminologyUniversité de MontréalConcordia University
FundersInstitut national des sciences mathématiques et de leurs interactionsNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueIsrael Science FoundationConcordia UniversityUniversité du Littoral Côte d'Opale
KeywordsMathematicsElliptic curvePhysicsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract We study the low‐lying zeros of a family of ‐functions attached to the complex multiplication elliptic curve , for each odd and square‐free integer . Specifically, upon writing the ‐function of as for the appropriate Grössencharakter of conductor , we consider the collection of ‐functions attached to , , where for each integer , denotes the primitive character inducing . We observe that of the ‐functions in have negative root number. is thus not one of the essentially homogeneous families of the universality conjecture of Sarnak, Shin and Templier [33], with unitary, symplectic or orthogonal (odd or even) symmetry type. By computing the one‐level density in the family of ‐functions in with conductor at most , we find that naturally decomposes into subfamilies: more specifically, a collection of symplectic ( for , even) and orthogonal ( for , odd) subfamilies. For each such subfamily, we moreover compute explicit lower order terms in decreasing powers of .

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 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.264
Teacher spread0.234 · 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 routes2
Has abstractyes

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