MétaCan
Menu
← Back to cohort

Experimental cosmic statistics - II. Distribution

2000· article· en· W2030800453 on OpenAlexaff
István Szapudi, Stéphane Colombi, Adrian Jenkins, J. M. Colberg

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical Astrophysics
Fundersnot available
KeywordsPhysicsSkewnessCOSMIC cancer databaseCosmic varianceProbability density functionStatisticsKurtosisGalaxyCosmic rayStatistical physicsAstrophysicsMathematicsRedshift

Abstract

fetched live from OpenAlex

Colombi et al. (Paper I) investigated the counts-in-cells statistics and their respective errors in the τCDM Virgo Hubble Volume simulation. This extremely large N-body experiment also allows a numerical investigation of the cosmic distribution function, ϒ (Ã), itself for the first time. For a statistic A, ϒ (Ã) is the probability density of measuring the value à in a finite galaxy catalogue. ϒ was evaluated for the distribution of counts-in-cells, PN, the factorial moments, Fk, and the cumulants, ξ̄ and SNs, using the same subsamples as Paper I. While Paper I concentrated on the first two moments of ϒ, i.e. the mean, the cosmic error and the cross-correlations, here the function ϒ is studied in its full generality, including a preliminary analysis of joint distributions ϒ (Ã,B̃). The most significant, and reassuring result for the analyses of future galaxy data is that the cosmic distribution function is nearly Gaussian provided its variance is small. A good practical criterion for the relative cosmic error is that ΔAA≲0.2. This means that for accurate measurements, the theory of the cosmic errors, presented by Szapudi & Colombi and Szapudi, Colombi & Bernardeau, and confirmed empirically by Paper I, is sufficient for a full statistical description and thus for a maximum likelihood rating of models. As the cosmic error increases, the cosmic distribution function ϒ becomes increasingly skewed and is well described by a generalization of the lognormal distribution. The cosmic skewness is introduced as an additional free parameter. The deviation from Gaussianity of ϒ(F̃k) and ϒ(S̃ N) increases with order k, N and similarly for ϒ(P̃ N) when N is far from the maximum of PN, or when the scale approaches the size of the catalogue. For our particular experiment, ϒ(F̃k) and ϒ(ξ ̄ ̃) are well approximated with the standard lognormal distribution, as evidenced by both the distribution itself and the comparison of the measured skewness with that of the lognormal distribution.

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)0.009
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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 designSimulation or modeling
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

Citations39
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueMonthly Notices of the Royal Astronomical Society→Same topicGalaxies: Formation, Evolution, Phenomena→French-language works237,207→