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Record W4367366695 · doi:10.48550/arxiv.2304.14187

Perturbative Correction to the Average Expansion Rate of Spacetimes with Perfect Fluids

2023· preprint· en· W4367366695 on OpenAlexfundno aff
Vincent Comeau

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesMcGill University
KeywordsPhysicsSpacetimeConstant (computer programming)Sign (mathematics)Perfect fluidScalar (mathematics)Mathematical physicsEquation of stateFirst orderTensor (intrinsic definition)Scalar fieldClassical mechanicsMathematical analysisMathematicsQuantum mechanicsApplied mathematicsGeometry

Abstract

fetched live from OpenAlex

This paper discusses the leading-order correction induced by cosmological perturbations on the average expansion rate of an expanding spacetime, containing one or many perfect fluids. The calculation is carried out up to the second order in the perturbations, and is kept as general as possible. In particular, no approximation such as a long-wavelength or a short-wavelength limit is invoked, and all three types of perturbations (scalar, vector, and tensor) are considered. First, the average value of the expansion rate is computed over a three-dimensional space-like surface where the total density of the fluids is constant. Then, a formula is derived relating that average value to the one over any other surface, on which a different scalar property of the fluids is constant. Moreover, the general formulas giving the correction to the average expansion rate are applied, in particular, to the case of a spacetime containing a single fluid with a constant equation of state. The sign and the effective equation of state of the corresponding back-reaction effect in the first Friedmann equation are examined.

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)0.008
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.191
Teacher spread0.160 · 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
Published2023
Admission routes1
Has abstractyes

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