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Record W1681986786 · doi:10.22323/1.138.0074

Quintessence, Neutrino Masses and Unification of the Dark Sector

2012· preprint· en· W1681986786 on OpenAlexafffund
Gennady Y. Chitov

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuintessencePhysicsNeutrinoMAJORANASterile neutrinoParticle physicsDark energyYukawa potentialMass generationDark matterNeutrino oscillationAstrophysicsCosmology

Abstract

fetched live from OpenAlex

The origin of the neutrino mass, dark matter (DM), and dark energy (DE) are among the most challenging problems of fundamental physics.We address these questions from analyses of the models where the neutrino mass is generated via Yukawa coupling to the quintessence field which represents the DE.It has been shown in a recent work on the toy model with a single Dirac fermion coupled to the quintessence that by choosing parameters of the DE potential to match the present DE density, the model allows to lock the neutrino mass at m ∼ 10 -2 eV and yields consistent estimates for other parameters of the Universe.To include the DM component into this framework we propose here to add the right-handed Majorana term(s) into Lagrangian with the quintessence-generated mass.As a results, the DE field is responsible for generation of the masses of the light active Majorana neutrinos as well as of the heavy sterile neutrinos.The latter are natural DM candidates.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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