Bulk viscosity analysis with new holographic dark energy in <i>f</i>(<i>R, T</i>) theory
Bibliographic record
Abstract
This paper represents the viscous circumstances to imitate new holographic dark energy behaviour in modified f( R, T) gravity theory with a flat Friedmann–Robertson–Walker model with bulk viscosity. Here, we assume bulk viscosity to be directly proportional to Hubble’s parameter ( ζ∝ H), i.e., ζ = ζ0 H, where ζ is assumed to be bulk viscosity coefficient, ζ0 is a positive constant, and H is Hubble’s parameter. In this particular model, we consider f( R, T) = R + λ T, where R represents the Ricci scalcalar, λ as a constant, and T as trace of the energy–momentum tensor. The focus of this article majorly revolves around the concept that the negative pressure induced by bulk viscosity could potentially act as the driving force behind dark energy, contributing to the expansion of the universe. We precisely determine the scale factor’s primary solution and examine all possible conditions: deceleration as well as acceleration, which contributes to the universe’s evolution. Depending on the values of the viscous terms, it has been observed that the model shows the decelerated period as well as the accelerated period. Later, we examine the diagnostic parameters, statefinder { r, s} to distinguish our model from the other existing dark energy models. Thus, a modified theory of gravity can explain the dark energy phenomenon in the presence of bulk viscosity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".