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Record W1997324115 · doi:10.1103/physrevd.74.123505

Integration of the Friedmann equation for universes of arbitrary complexity

2006· article· en· W1997324115 on OpenAlexafffund
Kayll Lake

Bibliographic record

VenuePhysical review. D. Particles, fields, gravitation, and cosmology/Physical review. D, Particles, fields, gravitation, and cosmology · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFriedmann–Lemaître–Robertson–Walker metricLambdaFunction (biology)Mathematical physicsMotion (physics)Epoch (astronomy)Constant (computer programming)Friedmann equationsGravitationPhysicsGravitational constantMathematicsCosmologyTheoretical physicsClassical mechanicsQuantum mechanicsComputer scienceDark energy

Abstract

fetched live from OpenAlex

An explicit and complete set of constants of the motion are constructed algorithmically for Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) models consisting of an arbitrary number of noninteracting species, each with a constant ratio of pressure to density. The inheritance of constants of the motion from simpler models as more species are added is stressed. It is then argued that all FLRW models admit a unique candidate for a gravitational epoch function---a function which gives a global time-orientation without reference to observers. The same relations that lead to the construction of constants of the motion allow an explicit evaluation of this function. In the simplest of all models, the $\ensuremath{\Lambda}\mathrm{CDM}$ model, it is shown that the epoch function exists for all models with $\ensuremath{\Lambda}>0$, but for almost no models with $\ensuremath{\Lambda}<0$.

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: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.028
GPT teacher head0.325
Teacher spread0.296 · 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

Citations8
Published2006
Admission routes2
Has abstractyes

Explore more

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