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Record W4386855795 · doi:10.1139/cjp-2023-0159

A comparative study of MOND and MOG theories versus the <i>κ</i>-model: an application to galaxy clusters

2023· article· en· W4386855795 on OpenAlexvenueno aff
G. Pascoli

Bibliographic record

VenueCanadian Journal of Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsModified Newtonian dynamicsTheoretical physicsDark matterGeneral relativityGalaxyCovariant transformationNewtonian dynamicsAstrophysicsMathematical physicsGalaxy rotation curveGalaxy formation and evolution

Abstract

fetched live from OpenAlex

Many models have been proposed to minimize the dark matter (DM) content in various astronomical objects at every scale in the Universe. The most widely known model is MOdified Newtonian dynamics (MOND). MOND was first published by Mordehai Milgrom in 1983. A second concurrent model is modified gravity, which is a covariant scalar–tensor–vector extension of general relativity. Other theories also exist but have not been broadly applied to a large list of astronomical objects. Eventually, we can also mention the Newtonian fractional-dimension gravity, a gravity theory based on spaces with fractional (i.e., non-integer) dimension. A new model, called κ-model, based on very elementary phenomenological considerations, has recently been proposed in the astrophysics field. This model shows that the presence of DM can be considerably minimized with regard to the dynamics of galaxies. The κ-model belongs to the general family of theories descended from MOND. Under this family of theories, there is no need to develop a highly uncertain DM sector of physics to explain the observations.

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)0.003
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 score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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