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Record W4396798743 · doi:10.31389/pop.17

Recovering General Relativity from a Planck Scale Discrete Theory of Quantum Gravity

2024· article· en· W4396798743 on OpenAlexfundno aff
Jeremy Butterfield, Fay Dowker

Bibliographic record

VenuePhilosophy of Physics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNoncommutative and Quantum Gravity Theories
Canadian institutionsnot available
FundersScience and Technology Facilities CouncilInstitut Périmètre de physique théorique
KeywordsQuantum gravityPlanck scalePhysicsLoop quantum gravityGeneral relativityTheoretical physicsTheory of relativityDoubly special relativityScale (ratio)PlanckClassical mechanicsQuantumMathematical physicsQuantum mechanics

Abstract

fetched live from OpenAlex

An argument is presented that if a theory of quantum gravity is physically discrete at the Planck scale and the theory recovers General Relativity as an approximation, then, at the current stage of our knowledge, causal sets must arise within the theory, even if they are not its basis. We lay out this argument in two claims. Roughly speaking, the first claim is that causal sets can recover continuum Lorentzian manifolds; and the second claim is that no other proposal for a set of discrete data that conforms to our sense of “fundamental discreteness at the Planck scale” is known to be able to recover continuum Lorentzian manifolds. To support this second claim, we show, in particular, that an apparent alternative discrete data set to causal sets, viz., a certain sort of combinatorial Lorentzian simplicial complex, cannot recover General Relativistic spacetimes in the appropriately unique way; for it cannot discriminate between Minkowski spacetime and a spacetime with a certain sort of gravitational wave burst.

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)0.002
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.009
Scholarly communication0.0020.006
Open science0.0010.004
Research integrity0.0010.004
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 head0.023
GPT teacher head0.271
Teacher spread0.249 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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