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Record W2521777928 · doi:10.1088/1361-6544/aa92e3

Scarring of quasimodes on hyperbolic manifolds

2017· preprint· en· W2521777928 on OpenAlexafffund
Suresh Eswarathasan, Lior Silberman

Bibliographic record

VenueNonlinearity · 2017
Typepreprint
Languageen
FieldMathematics
TopicMathematical Dynamics and Fractals
Canadian institutionsUniversity of British Columbia
FundersHeilbronn Institute for Mathematical ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsSemiclassical physicsGeodesicErgodic theorySubmanifoldMeasure (data warehouse)MathematicsManifold (fluid mechanics)Laplace operatorInvariant (physics)Operator (biology)CombinatoricsPure mathematicsPhysicsMathematical physicsMathematical analysisQuantum mechanicsQuantum

Abstract

fetched live from OpenAlex

Abstract Let N be a compact hyperbolic manifold, M ⊂ N an embedded totally geodesic submanifold, and let − ħ 2 Δ N be the semiclassical Laplace–Beltrami operator. For any ε > 0 we explicitly construct families of quasimodes of energy width at most ε ħ | log ⁡ ħ | which exhibit a ‘strong scar’ on M in that their microlocal lifts converge weakly to a probability measure which places positive weight on S ∗ M ( ↪ S ∗ N ) . An immediate corollary is that any invariant measure on S ∗ N occurs in the ergodic decomposition of the semiclassical limit of certain quasimodes of width ε ħ | log ⁡ ħ | .

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 score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.146
GPT teacher head0.401
Teacher spread0.255 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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