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Record W2725512993 · doi:10.1088/1361-6382/aadc36

Cosmological tests of Everpresent Λ

2018· article· en· W2725512993 on OpenAlexafffund
Nosiphiwo Zwane, Niayesh Afshordi, Rafael D. Sorkin

Bibliographic record

VenueClassical and Quantum Gravity · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersInstitut Périmètre de physique théorique
KeywordsPhysicsDark energyCosmologyRedshiftCosmological constantQuintessenceLambda-CDM modelAstrophysicsMetric expansion of spaceHubble's lawBaryon acoustic oscillationsPhysical cosmologyTheoretical physicsGalaxy

Abstract

fetched live from OpenAlex

Abstract Everpresent Λ is a cosmological scenario in which the observed cosmological ‘constant’ Λ fluctuates between positive and negative values with a vanishing mean, and with a magnitude comparable to the critical density at any epoch. In accord with a longstanding heuristic prediction of causal set theory, it postulates that Λ is a stochastic function of cosmic time that will vary from one realization of the scenario to another. Herein, we consider two models of ‘dark energy’ that exhibit these features. Via Monte Carlo Markov chains, we explore the space of cosmological parameters and the set of stochastic realizations of these models, finding that Everpresent Λ can fit current cosmological observations as well as the ΛCDM model does. Furthermore, it removes the observational tensions that ΛCDM experiences in relation to low redshift measurements of the Hubble constant, and to the baryonic acoustic oscillations (BAO) in Lyman- α forest at –3. It does not, however, help significantly with the early growth of ultramassive black holes, or with the Lithium problem in Big Bang nucleosynthesis. Future measurements of ‘dark energy’ at high redshifts will further test the viability of Everpresent Λ as an alternative to the ΛCDM 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 score0.085
Threshold uncertainty score0.327

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 studies0.0000.001
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 head0.016
GPT teacher head0.285
Teacher spread0.269 · 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

Citations24
Published2018
Admission routes2
Has abstractyes

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