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Record W2127570599 · doi:10.1063/1.3462601

Observationally Verifiable Predictions of Modified Gravity

2010· article· en· W2127570599 on OpenAlexafffund

Bibliographic record

VenueAIP conference proceedings · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersInstitut Périmètre de physique théoriqueNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsGravitationGravitational fieldCosmic microwave backgroundAction (physics)Dark energySpectral densityGalaxyGeneral relativityEquations of motion

Abstract

fetched live from OpenAlex

MOG is a fully relativistic modified theory of gravity based on an action principle. The MOG field equations are exactly solvable numerically in two important cases. In the spherically symmetric, static case of a gravitating mass, the equations also admit an approximate solution that closely resembles the Reissner‐Nordström metric. Furthermore, for weak gravitational fields, a Yukawa‐type modification to the Newtonian acceleration law can be obtained, which can be used to model a range of astronomical observations. Without nonbaryonic dark matter, MOG provides good agreement with the data for galaxy rotation curves, galaxy cluster masses, and gravitational lensing, while predicting no appreciable deviation from Einstein’s predictions on the scale of the solar system. Another solution of the field equations is obtained for the case of a a spatially homogeneous, isotropic cosmology. MOG predicts an accelerating universe without introducing Einstein’s cosmological constant; it also predicts a CMB acoustic power spectrum and a mass power spectrum that are consistent with observations without relying on non‐baryonic dark matter. Increased sensitivity in future observations or space‐based experiments may be sufficient to distinguish MOG from other theories, notably the ΛCDM “standard model” of cosmology.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.485

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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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 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

Citations2
Published2010
Admission routes2
Has abstractyes

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