MétaCan
Menu
Back to cohort
Record W4289709057 · doi:10.48550/arxiv.1808.01536

Displacement convexity of Boltzmann's entropy characterizes the strong\n energy condition from general relativity

2018· preprint· en· W4289709057 on OpenAlexfundno aff
Robert J. McCann

Bibliographic record

VenuearXiv (Cornell University) · 2018
Typepreprint
Languageen
FieldMathematics
TopicGeometric Analysis and Curvature Flows
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsGeodesicConvexityMathematicsRicci curvatureScalar curvatureCurvatureEntropy (arrow of time)Lorentz transformationManifold (fluid mechanics)HawkingMathematical analysisMathematical physicsPhysicsClassical mechanicsGeometryQuantum mechanics

Abstract

fetched live from OpenAlex

On a Riemannian manifold, lower Ricci curvature bounds are known to be\ncharacterized by geodesic convexity properties of various entropies with\nrespect to the Kantorovich-Rubinstein-Wasserstein square distance from optimal\ntransportation. These notions also make sense in a (nonsmooth) metric measure\nsetting, where they have found powerful applications. This article initiates\nthe development of an analogous theory for lower Ricci curvature bounds in\ntimelike directions on a (globally hyperbolic) Lorentzian manifold. In\nparticular, we lift fractional powers of the Lorentz distance (a.k.a. time\nseparation function) to probability measures on spacetime, and show the strong\nenergy condition of Hawking and Penrose is equivalent to geodesic convexity of\nthe Boltzmann-Shannon entropy there. This represents a significant first step\ntowards a formulation of the strong energy condition and exploration of its\nconsequences in nonsmooth spacetimes, and hints at new connections linking the\ntheory of gravity to the second law of thermodynamics.\n

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), Insufficient payload (model declined to judge)
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 score0.188
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.218
Teacher spread0.141 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)Same topicGeometric Analysis and Curvature FlowsFrench-language works237,207