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Record W4379518557 · doi:10.1139/cjp-2023-0054

Traversable wormhole models supported by a string cloud in rainbow gravity

2023· article· en· W4379518557 on OpenAlexvenueno aff
Umber Sheikh, Sana Arshad, Richard Pinčák

Bibliographic record

VenueCanadian Journal of Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicExperimental and Theoretical Physics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWormholePhysicsString (physics)RainbowExotic matterClassical mechanicsTheoretical physicsMathematical physicsQuantum mechanicsDark energy

Abstract

fetched live from OpenAlex

The traversable wormholes are fascinating as the shortcuts in space–time. This work discusses the geometry of a traversable wormhole with a string cloud as a source in rainbow gravity. The field equations are developed and solved to obtain the wormhole’s shape function, cloud’s mass density, and string tension. We have calculated the shape function and made it specific to study the dynamics for toy model. Based on the three well-known pairs of rainbow functions (mentioned by Ali and Khalil), the string cloud’s dynamical variables including mass density and string tension are graphically presented. The corresponding energy conditions are also visually depicted. It is found that the source matter (string cloud) of the traversable wormhole must be exotic. However, the positive values of the string tension led to the presence of Casimir and dark energy effects. It is found that the toy model respects the null energy condition for a single pair of rainbow functions, i.e., Rainbow Function Type II. We have concluded that in rainbow gravity theory, the traversable wormhole solutions originating from a string cloud requires the presence of exotic matter to achieve a stable structure.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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)0.0030.000

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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