MétaCan
Menu
Back to cohort
Record W3131334292 · doi:10.1016/j.anihpc.2021.01.005

Compactness of scalar-flat conformal metrics on low-dimensional manifolds with constant mean curvature on boundary

2021· article· en· W3131334292 on OpenAlexaff
Monica Musso, Juncheng Wei, Seunghyeok Kim

Bibliographic record

VenueAnnales de l Institut Henri Poincaré C Analyse Non Linéaire · 2021
Typearticle
Languageen
FieldMathematics
TopicNonlinear Partial Differential Equations
Canadian institutionsUniversity of British Columbia
FundersEngineering and Physical Sciences Research Council
KeywordsCompact spaceScalar curvatureMathematicsBoundary (topology)Conformal mapMathematical analysisTRACE (psycholinguistics)Compactness theoremScalar (mathematics)Constant (computer programming)CurvaturePure mathematicsSequence (biology)Geometry

Abstract

fetched live from OpenAlex

We concern C^{2} -compactness of the solution set of the boundary Yamabe problem on smooth compact Riemannian manifolds with boundary provided that their dimensions are 4, 5 or 6. By conducting a quantitative analysis of a linear equation associated with the problem, we prove that the trace-free second fundamental form must vanish at possible blow-up points of a sequence of blowing-up solutions. Applying this result and the positive mass theorem, we deduce the C^{2} -compactness for all 4-manifolds (which may be non-umbilic). For the 5-dimensional case, we also establish that a sum of the second-order derivatives of the trace-free second fundamental form is non-negative at possible blow-up points. We essentially use this fact to obtain the C^{2} -compactness for all 5-manifolds. Finally, we show that the C^{2} -compactness on 6-manifolds is true if the trace-free second fundamental form on the boundary never vanishes.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.316
Teacher spread0.275 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAnnales de l Institut Henri Poincaré C Analyse Non LinéaireSame topicNonlinear Partial Differential EquationsFrench-language works237,207