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Record W2135100458 · doi:10.1103/physreve.66.046136

Continuum percolation threshold for interpenetrating squares and cubes

2002· article· en· W2135100458 on OpenAlexaff
Don R. Baker, Gerald Paul, Sameet Sreenivasan, H. Eugene Stanley

Bibliographic record

VenuePhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsMcGill University
Fundersnot available
KeywordsPercolation thresholdPercolation (cognitive psychology)Monte Carlo methodCombinatoricsVolume fractionMathematicsCube (algebra)Square (algebra)Statistical physicsPhysicsGeometryStatisticsThermodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

Monte Carlo simulations are performed to determine the critical percolation threshold for interpenetrating square objects in two dimensions and cubic objects in three dimensions. Simulations are performed for two cases: (i) objects whose edges are aligned parallel to one another and (ii) randomly oriented objects. For squares whose edges are aligned, the critical area fraction at the percolation threshold ${\ensuremath{\varphi}}_{c}=0.6666\ifmmode\pm\else\textpm\fi{}0.0004,$ while for randomly oriented squares ${\ensuremath{\varphi}}_{c}=0.6254\ifmmode\pm\else\textpm\fi{}0.0002,$ 6% smaller. For cubes whose edges are aligned, the critical volume fraction at the percolation threshold ${\ensuremath{\varphi}}_{c}=0.2773\ifmmode\pm\else\textpm\fi{}0.0002,$ while for randomly oriented cubes ${\ensuremath{\varphi}}_{c}=0.2168\ifmmode\pm\else\textpm\fi{}0.0002,$ 22% smaller.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.012
GPT teacher head0.298
Teacher spread0.286 · 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

Citations129
Published2002
Admission routes1
Has abstractyes

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