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Record W1779157114 · doi:10.1139/cjp-2013-0434

Ghost dark energy models in Bianchi I cosmologies with modified gravity

2013· article· en· W1779157114 on OpenAlexvenueno aff
V. Fayaz, H. Hossienkhani, Ali Aghamohammadi, M. Amirabadi

Bibliographic record

VenueCanadian Journal of Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsDark energyQuintessenceEquation of stateCosmologyMetric expansion of spaceDeceleration parameterMathematical physicsAnisotropyUniverseDark fluidPhantom energyTheoretical physicsClassical mechanicsAstrophysicsQuantum mechanics

Abstract

fetched live from OpenAlex

A ghost dark energy model has been recently put forward to explain the current accelerated expansion of the universe. In this model, we develop the general scheme for modified f(R) gravity reconstruction from realistic anisotropic Bianchi type I cosmology. Power-law volumetric expansion is used to obtain exact solutions of the field equations. We discuss the physical behavior of the solutions and anisotropy behavior of the fluid, the expansion parameter, and the model in future evolution of the universe. We reconstruct corresponding f(R) gravities and obtain the equation of state parameter. We show that the corresponding f(R) gravity of the ghost dark energy model can behave like phantom or quintessence of the selected models that describe accelerated expansion of the universe.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.013
GPT teacher head0.208
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations4
Published2013
Admission routes1
Has abstractyes

Explore more

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