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Record W4367369747 · doi:10.48550/arxiv.2304.14341

A synthetic null energy condition

2023· preprint· en· W4367369747 on OpenAlexfundno aff
Robert J. McCann

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldMathematics
TopicGeometric Analysis and Curvature Flows
Canadian institutionsnot available
FundersDivision of Mathematical SciencesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNull (SQL)GeodesicMathematicsEnergy conditionRicci curvatureCurvatureGeneral relativityDifferential geometryMathematical analysisLimit (mathematics)Closed timelike curvePure mathematicsConvergence (economics)Dimension (graph theory)Closure (psychology)Boundary (topology)Limit of a sequenceConsistency (knowledge bases)Mathematical physicsSpacetimePhysicsDiscrete mathematicsGeometryQuantum mechanics

Abstract

fetched live from OpenAlex

We give a simplified approach to Kunzinger & Saemann's theory of Lorentzian length spaces in the globally hyperbolic case; these provide a nonsmooth framework for general relativity. We close a gap in the regularly localizable setting, by showing consistency of two potentially different notions of timelike geodesic segments used in the literature. In the smooth psuedo-Riemannian setting, we show Penrose' null energy condition is equivalent to a variable lower bound on the timelike Ricci curvature. This allows us to give a nonsmooth reformulation of the null energy condition using the timelike curvature-dimension conditions of Cavalletti \& Mondino (and Braun). Although this definition is consistent with the smooth setting, it proves unstable relative to the notion of pointed measured convergence for which timelike curvature-dimensions conditions are known to be stable. We illustrate this instability using a sequence of smooth weighted Lorentzian manifolds-with-boundary that satisfy it, yet converge to a disconnected pair of timelike related points that violate it in the limit.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.135
GPT teacher head0.216
Teacher spread0.081 · 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 teacher head, not a consensus.

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
Published2023
Admission routes1
Has abstractyes

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