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Record W2607350338 · doi:10.1088/1361-6382/aa7f0f

Holographic heat engines: general considerations and rotating black holes

2017· article· en· W2607350338 on OpenAlexafffund
Robie A. Hennigar, Fiona McCarthy, Alvaro Ballon, Robert B. Mann

Bibliographic record

VenueClassical and Quantum Gravity · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicBlack Holes and Theoretical Physics
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsBlack hole (networking)SpacetimeCarnot cycleWarp driveHeat engineClassical mechanicsCurvatureBlack hole thermodynamicsTheoretical physicsGeometryQuantum mechanicsEntropy (arrow of time)Thermodynamics

Abstract

fetched live from OpenAlex

Abstract We perform the first study of holographic heat engines where the working material is a rotating black hole, obtaining exact results for the efficiency of a rectangular engine cycle. We also make general considerations in the context of benchmarking these engines on circular cycles. We find an exact expression that is valid for black holes with vanishing specific heat at constant volume and derive an upper bound, below the Carnot efficiency and independent of spacetime dimension, which holds for any black hole of this kind. We illustrate our results with applications to a variety of black holes, noting the effects of spacetime dimension, rotation, and higher curvature corrections on the efficiency of the cycle.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.003
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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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

Citations88
Published2017
Admission routes2
Has abstractyes

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