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Record W1538789665 · doi:10.48550/arxiv.gr-qc/0606108

Quantum Gravity and Precision Tests

2006· preprint· en· W1538789665 on OpenAlexfundno aff
C. P. Burgess

Bibliographic record

VenueArXiv.org · 2006
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaKillam TrustsMcMaster University
KeywordsQuantum gravityPhysicsGeneral relativityQuantization (signal processing)Theoretical physicsParallelsEffective field theoryQuantum field theoryQuantumClassical mechanicsQuantum mechanicsComputer scienceAlgorithm

Abstract

fetched live from OpenAlex

This article provides a cartoon of the quantization of General Relativity using the ideas of effective field theory. These ideas underpin the use of General Relativity as a theory from which precise predictions are possible, since they show why quantum corrections to standard classical calculations are small. Quantum corrections can be computed controllably provided they are made for the weakly-curved geometries associated with precision tests of General Relativity, such as within the solar system or for binary pulsars. They also bring gravity back into the mainstream of physics, by showing that its quantization (at low energies) exactly parallels the quantization of other, better understood, non-renormalizable field theories which arise elsewhere in physics. Of course effective field theory techniques do not solve the fundamental problems of quantum gravity discussed elsewhere in these pages, but they do helpfully show that these problems are specific to applications on very small distance scales. They also show why we may safely reject any proposals to modify gravity at long distances if these involve low-energy problems (like ghosts or instabilities), since such problems are unlikely to be removed by the details of the ultimate understanding of gravity at microscopic scales.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.282
Teacher spread0.261 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2006
Admission routes1
Has abstractyes

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