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Record W3024545891 · doi:10.1088/1361-6382/ab9bbb

Dynamics of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>Z</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> symmetric EdGB gravity in spherical symmetry

2020· article· en· W3024545891 on OpenAlexfundno aff

Bibliographic record

VenueClassical and Quantum Gravity · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicBlack Holes and Theoretical Physics
Canadian institutionsnot available
FundersCanadian Institute for Advanced ResearchSimons FoundationNational Science Foundation
KeywordsScalar fieldCircular symmetryBlack hole (networking)DilatonScalar (mathematics)Schwarzschild metricSchwarzschild radiusCoupling (piping)

Abstract

fetched live from OpenAlex

Abstract We report on a numerical investigation of black hole evolution in an Einstein dilaton Gauss–Bonnet (EdGB) gravity theory where the Gauss–Bonnet coupling and scalar (dilaton) field potential are symmetric under a global change in sign of the scalar field (a ‘ Z 2 ’ symmetry). We find that for sufficiently small Gauss–Bonnet couplings Schwarzschild black holes are stable to radial scalar field perturbations, and are unstable to such perturbations for sufficiently large couplings. For the latter case, we provide numerical evidence that there is a band of coupling parameters and black hole masses where the end states are stable scalarized black hole solutions, in general agreement with the results of Macedo et al (2019 Phys. Rev. D 99 104041). For Gauss–Bonnet couplings larger than those in the stable band, we find that an ‘elliptic region’ forms outside of the black hole horizon, indicating the theory does not possess a well-posed initial value formulation in that regime.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.231
Teacher spread0.218 · 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

Citations67
Published2020
Admission routes1
Has abstractyes

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