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Record W3046677573 · doi:10.1139/cjp-2020-0037

Dynamics of a quintom dark energy model non-minimally coupled with a mixed kinetic geometric term

2020· article· en· W3046677573 on OpenAlexvenueno aff
Mihai Marciu

Bibliographic record

VenueCanadian Journal of Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsDark energyBig CrunchSingularityGravitational singularityKinetic energyContext (archaeology)Kinetic termClassical mechanicsExponential functionTheoretical physicsUniverseStatistical physicsDe Sitter universeCosmologyMathematical analysisQuantum mechanics

Abstract

fetched live from OpenAlex

Within this work, a new dark energy model is proposed by taking into account a non-minimally mixed kinetic geometric coupling between the two fields, which constructs a quintom model. By considering numerical analysis of the corresponding field equations, the evolution of the Universe in the present model has been analyzed, taking into account different viable potentials. For exponential potentials, the model presents an accelerated expansion, and the dark energy equation of state exhibits phantom divide line crossing. However, for a steeper potential, for example, a mixed potential, it is observed that the Universe can manifest Big Crunch singularities in the distant future, and the moment for which the singularity occurs is sensitive to the value of the mixed coupling strength embedded into the η coefficient. The results show that an increase of the η parameter induces a delay in the Big Crunch time in the distant future. In the present context, the choice of the potential function plays a fundamental role in the evolution of the dynamical system, leading to very distinct cosmological scenarios.

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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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